Exploring the Potential of Brain-Computer Interfaces in Managing Alzheimer\'s disease: A Review

Abstract

Brain-computer interfaces (BCIs) have the potential to provide new communication avenues for individuals with Alzheimer\'s disease, a condition that often leads to cognitive decline and difficulty in communicating with others. However, the development and implementation of BCIs for this population pose several significant challenges. In this paper, the current state of BCI research for individuals with Alzheimer\'s disease is reviewed, with a focus on the use of BCI technology for communication. The limitations and challenges associated with BCI implementation in this population, including issues related to data interpretation and translation, usability for individuals with cognitive decline, and long-term effectiveness, are highlighted. Despite these challenges, the potential of BCI technology to enhance communication in individuals with Alzheimer\'s disease is discussed. It is noted that BCI technology has the potential to provide new communication avenues for individuals with Alzheimer\'s disease, including non-verbal communication and control of assistive devices. However, further research is needed to advance the field and overcome the challenges associated with BCI implementation in this population. The review concludes with a discussion of the ethical and privacy concerns associated with the use of BCI technology in individuals with Alzheimer\'s disease, and the need for ethical guidelines to ensure the responsible use of BCI technology for this population. Overall, this paper provides a comprehensive overview of the current state of BCI research for individuals with Alzheimer\'s disease, highlighting the challenges and limitations as well as the potential for BCI technology to enhance communication and improve quality of life for this population.

Introduction

I. BRAIN COMPUTER INTERFACE TECHNOLOGY- AN INTRODUCTION

Brain-computer interfaces (BCIs) have been proposed as a potential therapeutic tool for Alzheimer's disease patients. BCIs use electrodes placed on the scalp to record brain activity and translate it into control signals for a computer or other device. In Alzheimer's disease, BCIs have been shown to improve cognitive function and quality of life, particularly in the areas of memory, attention, and executive function. However, more research is needed to fully understand the potential benefits and limitations of BCIs for Alzheimer's patients. BCIs may also have the potential to aid in early diagnosis and monitoring of the disease progression. Despite the promise, the development of BCIs for Alzheimer's disease is still in its early stages, and further research is needed to determine their effectiveness and feasibility as a treatment option. Brain computer interface is one of the most promising and increasingly popular technologies for assisting and improving communication/control for motor paralysis (e.g., paraplegia or quadriplegia) due to stroke, spinal cord injury, cerebral palsy, and amyotrophic lateral sclerosis (ALS). Eye-tracking technology also allows paralyzed people to control external devices but it has many drawbacks due to the way of measuring the eye movements via cameras or using attached electrode on face such as electrooculography (EOG) signals. BCI essentially involves translating human brain activity into external action by sending neural commands to external devices (Belkacem et al., 2015a, 2018; Gao et al., 2017; Chen et al., 2020; Shao et al., 2020). Although, the most common use of BCI is to help disabled people with disorders in the motor system, it might be very useful tool for improving the quality of life of healthy people, particularly the elderly. Assistive, adaptive, and rehabilitative BCI applications for older adults and elderly patients should be developed to assist with their domestic chores, enhance relationships with their families and improve their cognitive and motor abilities. BCI technology has clinical and non-clinical applications in many areas, including medicine, entertainment, education, and psychology to solve many health issues such as cognitive deficits, slowness in processing speed, impaired memory and movement capability decline among elderly people. These issues can affect the quality of elderly life and may have adverse effects on mental health. To help older people maintain a healthy, good quality of life and sense of wellbeing, many BCI applications have been developed in the past decade.

To build a BCI system, five or six components are generally needed:

1. Signal acquisition during a specific experimental paradigm,
2. Pre-processing,
3. Feature extraction (e.g., P300 amplitude, SSVEP, or alpha/beta bands),
4. Classification (detection),
5. Translation of the classification result to commands (BCI applications), and
6. User feedback.

For quick and accurate processing and analysis of brain data, researchers have developed many open source software packages and toolboxes such as

a. BCI20001

b. EEGLab2

c. FieldTrip3

d. Brainstorm4

These software packages are based on advanced signal and image processing methods and artificial intelligence programs for performing sensor or source level analyses (Belkacem et al., 2015b, 2020; Dong et al., 2017).

A. History of BCI

Research in the field of brain-computer interfaces (BCI) has started in the 70s at the University of California (UCLA), Los Angeles, under a grant from the National Science Foundation. The paper “Toward Direct Brain-Computer Communication”, by Jacque Vidal can be considered a pioneer scientific publication, describing the study of BCI [1]. The very first international conference on BCI took place in 1999 (New York), where Jonathan R.Wolpaw formalized the definition of a BCI system [2]: “A brain-computer interface (BCI) is a communication or control system in which the user’s messages or commands do not depend on the brain’s normal output channels. That is, the message is not carried by nerves and muscles and furthermore, neuromuscular activity is not needed to produce the activity that does carry the message”.[1]

Brain-Computer Interfaces (BCIs) have a long history of development, stretching back over several decades. The idea of using brain signals to control computers and other devices can be traced back to the 1970s and 1980s, when researchers first started experimenting with EEG and other brain signals as a way to control simple devices such as lights and cursors on a computer screen.

In the 1990s and early 2000s, BCIs evolved from simple demonstrations of proof-of-concept to more complex and sophisticated systems, such as P300 spellers and movement-imagery-based BCIs. These systems showed that it was possible to use brain signals to control computers and other devices with a high degree of accuracy, opening the door for further development of BCIs for practical applications.

In recent years, the development of BCIs has been driven by advancements in neuroscience, computer science, and engineering. New methods for decoding brain signals, such as machine learning algorithms and deep neural networks, have allowed BCIs to become more reliable and accurate, and have opened up new applications for BCIs in areas such as rehabilitation, communication, and entertainment. At the same time, advances in implantable and wearable devices have made BCIs more accessible and practical for a wider range of users, including those with disabilities and elderly populations. Today, BCIs are being developed for a wide range of applications, including control of prosthetic limbs, communication aids for people with speech and motor impairments, and tools for monitoring and managing neurological and psychiatric conditions such as Alzheimer's disease and depression.

Overall, the history of BCIs reflects a continuous evolution of technology and ideas, driven by the desire to use brain signals to improve human life. Despite the many challenges that remain, BCIs are poised to play an increasingly important role in the years to come, as researchers continue to develop new applications and push the boundaries of what is possible with these fascinating technologies.

II. LITERATURE SEARCH

Mahshad Ouchani et al[2] The authors of this study used EEG band forces, coherence, dominant frequency, peak frequency, and cortical sources to distinguish sixteen patients with AD from nineteen patients by frontotemporal dementia. The most accurate predictors of frontotemporal dementia and AD were identified in a model using logistic regression analysis. Activities such as elevated levels of visuospatial capacity and episodic memory were among the predictors. The model’s classification accuracy was 93.3 percent.

Obtaining EEG data from MCI or AD patients is currently very complicated. In comparison to ECG and other biomedical records, such databases are not open to the public. As a consequence, consistently benchmarking and evaluating the latest approaches for the detection of Alzheimer’s disease from EEG signals are difficult. Furthermore, almost none of those techniques integrate biophysical information about AD; comprehensive mathematical models of AD pathology combined with EEG data analysis can aid in improving AD diagnosis.

 Acetylcholinesterase was observed when the radioligands C-PMP and C-MP4A were utilised. This finding indicates a reduction in the temporal lobes of the AD subjects [3]. The same decline was observed amongst subjects with MCI, which eventually progressed to AD. The subjects with AD and neurodegenerative dementia were further classified. A-beta amyloid-specific ligands (Pittsburgh compound B 11C-PIB) were used because the subjects with AD showed improvements relative to the subjects with fronto-temporal lobar degeneration (FTLD) and Parkinson's disease (PD) [4].

Chuck Eaststom [5] et al provides a functional model for unifying the terminology used in the field of brain-computer interfaces (BCIs). The purpose of the model is to provide a standardized way of communicating about BCIs and to help researchers and practitioners in the field to better understand and use the terminology associated with BCIs. The paper proposes a functional model that consists of three main parts: the input, the processing, and the output. The input refers to the signals that are obtained from the brain, such as electroencephalogram (EEG) signals, magnetic resonance imaging (MRI) signals, and functional near-infrared spectroscopy (fNIRS) signals. The processing refers to the algorithms and methods used to analyze and interpret the input signals, such as machine learning algorithms, signal processing techniques, and feature extraction methods. The output refers to the result of the processing, such as the identification of patterns in the signals, the prediction of certain events or outcomes, or the control of devices or systems. The functional model provides a framework for organizing and categorizing the various components and processes involved in BCIs and helps to clarify the relationships between the various elements of a BCI system.

Dong-Kyun Han [6] et al presents a framework for improving the performance of Brain-Computer Interfaces (BCIs) in session-independent settings. BCIs are systems that allow users to control devices and perform various tasks using their brain activity. However, the performance of BCIs can be affected by variations in the user's brain activity across different sessions, which is referred to as the session-dependence problem. To address this issue, the authors propose a domain generalization approach, which aims to improve the generalization of BCI models across different sessions by reducing the domain shift between the training and test data. The framework involves training a BCI model using a combination of labeled and unlabeled data from multiple sessions, and using a domain adaptation technique to adjust the model's parameters to reduce the domain shift. The authors evaluate the proposed framework on real-world BCI data and show that it improves the performance of BCIs in session-independent settings compared to traditional BCI models.

Javier Fumanal-Idocin [7] et al discusses a method for improving motor-imagery-based Brain-Computer Interfaces (BCI) by using signal derivation and aggregation functions. The authors propose the use of these functions to process EEG signals and extract relevant information for BCI control. The goal of the approach is to improve the performance and reliability of BCI systems by reducing the influence of noise and other confounding factors. The proposed method is evaluated using a dataset of EEG signals recorded from participants performing motor-imagery tasks, and the results show improved performance compared to traditional methods. The BCI system is designed to analyze electroencephalography (EEG) signals recorded from the brain during motor imagery tasks and translate them into commands for control of assistive devices. The traditional method of BCI relies on feature extraction and classification techniques, however, this paper proposes to use signal derivation and aggregation functions to improve the performance of the BCI system.

The paper, "A Systematic Deep Learning Model Selection for P300-Based Brain–Computer Interfaces" by Berdakh Abibullaev, [8] presents a systematic approach for selecting a deep learning model for P300-based Brain-Computer Interfaces (BCIs). The P300 response is an event-related potential that occurs in response to an unusual or unexpected event. In BCI systems, P300 is used to detect user intentions or selections from a set of stimuli. The paper focuses on using deep learning models to enhance the performance of P300-based BCIs. The authors evaluate a number of deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders, and compare their performance using different metrics. The authors then use a systematic approach to select the best deep learning model for P300-based BCIs. The results of the study indicate that the use of deep learning models can significantly improve the performance of P300-based BCIs, compared to traditional methods.

By Brent J. Lance [9] et al provides a perspective on the future of Brain-Computer Interface (BCI) technologies, focusing on advancements and advancements that can be expected in the coming decades. It may discuss challenges, current limitations, and new opportunities in the field of BCI, along with the potential impact of these technologies on various areas of human life.

The paper may also provide an overview of the state-of-the-art BCI systems, their design principles, and their performance evaluation. Additionally, it may analyze the latest trends and innovations in BCI and suggest future directions for research and development in the field. There are several challenges in the development and implementation of Brain-Computer Interfaces (BCI) technology. Some of these include:

1. Signal quality and stability: BCI relies on the measurement and interpretation of brain signals, which can be influenced by various factors such as noise, movement, and muscle activity. Maintaining the quality and stability of these signals is crucial for the accuracy and reliability of BCI systems.
2. User training: BCI systems often require users to undergo extensive training to learn how to generate and control specific brain signals. This can be a time-consuming and challenging process, especially for individuals with disabilities or limited motor control.
3. Performance variability: BCI performance can vary greatly across users and even within the same user over time, making it difficult to consistently and accurately control the interface.
4. Lack of standardization: There is currently no standard method for measuring and interpreting brain signals, which makes it difficult to compare results across different BCI systems and applications.
5. Ethical and privacy concerns: BCI raises several ethical and privacy concerns, such as the potential for brain signals to be used for surveillance or control without a user's consent.

Sheng-Ta Hsieh [10] et al focuses on developing a 2-channel brain-computer interface (BCI) using embedded systems and Bluetooth technology. The BCI is designed to perform attention experiments and observe the correlation between brainwave and attention. The design of the BCI includes an ECG/EMG electrode, a pre-amplifier, a band-pass filter, an analog-to-digital converter, and an amplifier. The AD620 is used as the instrumentation amplifier with a gain of 50, and the R-C passive filter is used as the high and low-pass filter. The gain amplifier adopts the OP07 for its low input offset voltage, high bandwidth, and low noise. The BCI is designed to be small and portable, with the ability for real-time processing.

Abdelkader Nasreddine Belkacem [11] et al focuses on the role of Brain Computer Interfaces (BCI) in improving the quality of life for older adults and elderly patients. It highlights the benefits of using BCI as a non-invasive solution for various health challenges faced by this population, such as physical disabilities, cognitive decline, and neurological conditions. BCI works by allowing communication between the brain and a computer, which can be achieved through different methods such as EEG or fMRI scans. This technology can be used to assist with tasks such as controlling prosthetic limbs, improving communication, and increasing mobility. Additionally, BCI has the potential to improve cognitive function and memory in older adults and elderly patients. The paper highlights that the use of BCI for elderly populations has the potential to greatly improve their quality of life, offering life-changing solutions to various health challenges. Furthermore, ongoing research and development in this field may lead to the creation of even more innovative and effective BCI solutions in the future. In conclusion, the paper argues that BCI has the potential to greatly improve the lives of older adults and elderly patients, and that ongoing research and development in this field is essential to achieve this goal.

Remigiusz J. Rak [12] et al Brain-Computer Interfaces (BCI) are discussed as a type of control system. BCI allows communication between the brain and a computer, enabling the brain to control devices and systems in real-time. The technology can be used to control various devices, such as prosthetic limbs, wheelchairs, or gaming devices, by interpreting the user's intentions through brain activity.mThe paper reviews the current state of BCI technology, including its limitations and challenges, and provides an overview of its applications in different fields, such as medicine, gaming, and robotics. The paper also discusses how BCI can be used as a control system in these fields, allowing users to control devices through their thoughts.The paper concludes by highlighting the potential of BCI as a promising technology for control systems in various areas. With ongoing research and development, it is expected that BCI will continue to advance and offer even more innovative and effective control solutions in the future.

Dilek Manzak [13] et al proposes a method for the automated classification of Alzheimer's Disease (AD) using a deep neural network (DNN) and random forest feature elimination. In this study, the author uses a DNN to classify AD based on structural magnetic resonance imaging (MRI) data. The DNN is trained on a large dataset of MRI scans and is used to predict the likelihood of AD. However, the large number of features in the MRI data can lead to overfitting, which can negatively impact the accuracy of the classifier. To address this issue, the author proposes using random forest feature elimination to reduce the number of features in the MRI data. This is accomplished by using a random forest classifier to rank the importance of the features and removing the least important features until the accuracy of the classifier reaches a maximum. The results of the study show that the proposed method is able to achieve high accuracy in classifying AD using MRI data, with a sensitivity of 90% and a specificity of 86%.

The author also provides a comparison with other methods for classifying AD, including support vector machines (SVMs) and k-nearest neighbors (KNNs). In conclusion, this paper presents a novel method for the automated classification of Alzheimer's Disease using a DNN and random forest feature elimination. The results demonstrate the potential of this method for improving the accuracy of AD diagnosis using MRI data.

Fuente Garciaa [14] et al is a systematic review of the various Artificial Intelligence, Speech, and Language Processing approaches used for monitoring Alzheimer's disease. The authors aim to provide an overview of the current state-of-the-art in this field and identify areas for future research. The paper summarizes the different approaches used for speech and language analysis, including acoustic and prosodic analysis, and language modeling. The authors also discuss the challenges and limitations of using AI for monitoring Alzheimer's disease and suggest potential solutions to overcome these obstacles. The paper concludes by highlighting the need for further research in this area to develop more effective and reliable AI-based approaches for monitoring Alzheimer's disease.

In Maria Luisa Barragan Pulido [15] et al the authors discuss the challenges posed by the degenerative nature of Alzheimer's disease and the impact it has on speech and language abilities. The paper reviews various speech analysis techniques, including acoustic, prosodic, and language modeling, that have been used to monitor the disease and assess its progression. The authors also discuss the limitations of current approaches and highlight the need for further research to develop more robust and accurate methods for monitoring Alzheimer's disease using speech analysis. The paper concludes by emphasizing the potential of speech analysis as a tool for early diagnosis and monitoring of Alzheimer's disease.

In the research work of “Brain-Computer Interface to Enhance Attention in Alzheimer’s disease” Melanie Fried-Oken, [16] reports on a research project funded by the National Institute on Deafness and Other Communication Disorders to develop a Brain-Computer Interface (BCI) to enhance attention in Alzheimer's disease. The project was led by Melanie Fried-Oken and was based at Oregon Health & Science University. The study aimed to explore the feasibility of using BCI as a tool to improve attention in individuals with Alzheimer's disease and to understand the underlying mechanisms of BCI-enhanced attention. The project was conducted over a three-year period from 2018 to 2021 and involved the development and testing of a BCI system to enhance attention in Alzheimer's disease patients. The results of the study provide insight into the potential of BCI as a tool for enhancing attention and improving quality of life for individuals with Alzheimer's disease.

Jamal F. Hwaidhil et al [17] describes a study that aimed to develop a new method for classifying motor imagery EEG signals, which are signals produced by the brain when a person is imagining movements. The method proposed in the paper uses a combination of two deep learning models: a deep autoencoder and a Convolutional Neural Network (CNN). A deep autoencoder is a type of neural network that is trained to reconstruct its inputs. The authors used the deep autoencoder to extract features from the EEG signals, which were then fed into a CNN for classification. The CNN is a type of neural network that is specifically designed for image recognition and has proven to be effective in various EEG classification tasks. The authors evaluated the performance of their approach on a publicly available EEG dataset, which contained EEG signals from subjects imagining different movements such as left and right hand movements. The results showed that the proposed method outperformed existing methods for EEG-based motor imagery classification and provided a promising solution for this problem. In conclusion, the authors argue that the combination of a deep autoencoder and a CNN has the potential to be a useful tool for classifying motor imagery EEG signals and can be applied in areas such as brain-computer interfaces and rehabilitation.

In Tin Shi Lee et al [18] presents a pilot study investigating the use of a Brain-Computer Interface (BCI) based cognitive training system for healthy elderly individuals. The study aims to evaluate the usability and preliminary efficacy of the system in improving cognitive function. The BCI system developed in the study is designed to enhance cognitive function through the use of cognitive training tasks that are controlled by EEG signals. Participants in the study were randomly assigned to either an experimental group, which received the BCI-based cognitive training, or a control group, which received traditional cognitive training. The participants were evaluated before and after the training to assess changes in cognitive function. The results of the study showed that the BCI-based cognitive training was well-received by the participants and was deemed to be easy to use. The study also found preliminary evidence of the efficacy of the BCI-based cognitive training in improving cognitive function, as the experimental group showed significant improvements in cognitive test scores compared to the control group.In conclusion, the study provides initial evidence for the feasibility and potential efficacy of using a BCI-based cognitive training system for healthy elderly individuals. The authors suggest that further research is needed to validate these findings and to determine the long-term impact of BCI-based cognitive training on cognitive function.

Andreas Miltiadous et al [19] focuses on developing a reliable method for diagnosing Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) based on EEG signals. In this study, the authors propose a classification method that uses EEG signals to diagnose AD and FTD, two types of dementia that cause significant memory loss and cognitive decline in older adults. The classification method is based on statistical features extracted from the EEG signals and a machine learning algorithm. To validate the accuracy of the proposed method, the authors compare different validation methods, including cross-validation and leave-one-out validation. The results show that the proposed method has high accuracy and stability, demonstrating its potential as a valuable tool for diagnosing AD and FTD. The authors also discuss the limitations of the study, including the small sample size and the need for further validation using larger datasets. Nevertheless, the results of this study provide a promising basis for further research in the field of EEG-based diagnosis of AD and FTD. In conclusion, this paper presents a robust classification method for diagnosing AD and FTD using EEG signals, and compares different validation methods to demonstrate the accuracy of the method. The results of this study have the potential to contribute to the development of more effective and efficient diagnostic tools for dementia.

In Sithara Afsal et al, [20] the paper presents a framework for handling the class imbalance problem in Alzheimer's stage detection. The class imbalance problem refers to a situation in which the number of samples for one class is much larger than that for the other class, which can lead to poor performance of machine learning algorithms. In the context of Alzheimer's stage detection, the class imbalance problem arises because the number of samples for healthy individuals is much larger than the number of samples for individuals with Alzheimer's disease. To address this problem, the authors propose a data augmentation based framework for Alzheimer's stage detection. The framework consists of two steps: data augmentation and classifier training. In the data augmentation step, the authors use various techniques such as oversampling and undersampling to balance the class distribution. In the classifier training step, the authors train a machine learning classifier using the augmented data to detect Alzheimer's disease. The authors evaluate the performance of their framework on a publicly available dataset and compare it with several state-of-the-art methods. The results show that the proposed framework outperforms existing methods in terms of accuracy and provides a promising solution for the class imbalance problem in Alzheimer's stage detection. In conclusion, the authors argue that their data augmentation based framework is a promising solution for handling the class imbalance problem in Alzheimer's stage detection and can be used to improve the accuracy of machine learning algorithms for this task.

Niago Moreira Nobre Leite et al [21] a method for filtering EEG (electroencephalogram) noise using a deep convolutional autoencoder. EEG signals are widely used in brain-computer interfaces, neuroscience, and clinical diagnosis, but the signals are often contaminated by various types of noise such as electrical interference, movement artifacts, and other biological signals. Removing these noises is crucial for accurately interpreting EEG signals.The authors propose a deep convolutional autoencoder, a type of deep learning model, for filtering EEG noise. The autoencoder is trained on EEG signals that have been contaminated with different types of noise. The training process learns a mapping from the contaminated signals to the corresponding clean signals, which can then be used to filter new EEG signals. The authors evaluate the performance of their method on a publicly available EEG dataset that contains both clean and contaminated signals. The results show that the deep convolutional autoencoder is able to effectively remove EEG noise and improve the quality of the EEG signals. In conclusion, the authors argue that the deep convolutional autoencoder provides a promising solution for filtering EEG noise and can be used to improve the accuracy of EEG-based applications such as brain-computer interfaces, neuroscience, and clinical diagnosis.

Yang Qiu et al [22] presents a method for denoising ictal EEG (electroencephalogram) signals using a denoising sparse autoencoder. Ictal EEG signals are recordings of brain activity during seizures, and are commonly used for the diagnosis of epilepsy. However, ictal EEG signals are often contaminated by various types of noise, making it challenging to accurately interpret the signals.The authors propose a denoising sparse autoencoder, a type of deep learning model, for denoising ictal EEG signals. The autoencoder is trained on clean and contaminated ictal EEG signals, where the clean signals are used as the ground truth. The training process learns a mapping from the contaminated signals to the corresponding clean signals, which can then be used to denoise new ictal EEG signals.The authors evaluate the performance of their method on a publicly available ictal EEG dataset that contains both clean and contaminated signals. The results show that the denoising sparse autoencoder is able to effectively remove the noise from the ictal EEG signals and improve the quality of the signals.In conclusion, the authors argue that the denoising sparse autoencoder provides a promising solution for denoising ictal EEG signals and can be used to improve the accuracy of epilepsy diagnosis based on ictal EEG recordings.

In Yongli Shang et al [23] presents a method for recognizing emotions based on multimodal data using a deep autoencoder. The method combines EEG (electroencephalogram) signals, which measure brain activity, and facial expressions, which are a visual representation of emotions. The goal is to accurately recognize emotions based on the combined information from both modalities. The authors propose a deep autoencoder, a type of deep learning model, for combining EEG and facial expression information.

The autoencoder is trained on EEG and facial expression data that have been labeled with the corresponding emotions. The training process learns a mapping from the EEG and facial expression data to the emotions, which can then be used to recognize emotions in new EEG and facial expression data. The authors evaluate the performance of their method on a publicly available dataset that contains EEG, facial expression, and emotion data. The results show that the deep autoencoder is able to effectively recognize emotions based on the combination of EEG and facial expression information. In conclusion, the authors argue that the deep autoencoder provides a promising solution for recognizing emotions based on multimodal data and can be used to improve the accuracy of emotion recognition in various applications such as human-computer interaction and mental health diagnosis.

In Chuan Jia, Xiaorong Gao et al [24] presents a method for improving the accuracy of a SSVEP (Steady-State Visual Evoked Potential) based brain-computer interface. SSVEP-based brain-computer interfaces use EEG (electroencephalogram) signals to detect the frequency and phase of visual stimuli presented to a user and use this information to control a computer. The authors propose a method called frequency and phase mixed coding for improving the accuracy of SSVEP-based brain-computer interfaces. The method combines multiple visual stimuli with different frequencies and phases, allowing the EEG signals to provide more information about the user's intent. The authors evaluate the performance of their method on a publicly available dataset that contains EEG signals from users who were presented with different visual stimuli. The results show that frequency and phase mixed coding improves the accuracy of SSVEP-based brain-computer interfaces compared to traditional methods that use a single frequency and phase for visual stimulation. In conclusion, the authors argue that frequency and phase mixed coding provides a promising solution for improving the accuracy of SSVEP-based brain-computer interfaces and can be used to develop more effective and user-friendly brain-computer interfaces for various applications.

Rajdeep Gosh et al [25] presents a method for removing eye blink artifacts from EEG (electroencephalogram) signals using support vector machine and autoencoder. Eye blink artifacts are a common problem in EEG signals and can interfere with the accurate analysis of brain activity. The authors propose a method that combines a support vector machine and an autoencoder for removing eye blink artifacts from EEG signals. The support vector machine is used to identify the locations of eye blinks in the EEG signals, while the autoencoder is used to reconstruct the EEG signals without the eye blink artifacts. The authors evaluate the performance of their method on publicly available EEG datasets that contain eye blink artifacts. The results show that the method is effective at removing eye blink artifacts from EEG signals and that the reconstructed signals accurately reflect the underlying brain activity. In conclusion, the authors argue that their method provides a promising solution for removing eye blink artifacts from EEG signals and can be used to improve the accuracy of EEG-based analysis in various applications, such as brain-computer interfaces and cognitive neuroscience research.

The paper, "An Introduction to Deep Learning Research for Alzheimer's Disease" by Hoang Nguyen [26] et al provides an overview of the use of deep learning in research related to Alzheimer's disease. The paper starts by introducing the concept of deep learning and how it differs from traditional machine learning methods. It then goes on to describe the various types of deep learning algorithms, such as convolutional neural networks and recurrent neural networks, and how they can be applied to Alzheimer's disease research. The paper also discusses the challenges faced when using deep learning for Alzheimer's disease research, such as the limited availability of labeled data and the high computational demands of deep learning algorithms. The paper concludes by summarizing the current state of deep learning research in Alzheimer's disease, including recent studies that have used deep learning to classify Alzheimer's disease and predict its progression, and highlighting the potential of deep learning to make significant contributions to Alzheimer's disease research in the future. In conclusion, the paper provides a comprehensive introduction to the use of deep learning in Alzheimer's disease research, highlighting its potential and the challenges faced. It can serve as a useful resource for researchers and practitioners who are interested in using deep learning for Alzheimer's disease research.

There are several challenges faced by Alzheimer's disease researchers when using deep learning algorithms:

a. Limited Availability of Labeled Data: One of the biggest challenges in Alzheimer's disease research is the limited availability of labeled data. Deep learning algorithms require large amounts of labeled data to train the model, but obtaining this data can be difficult in Alzheimer's disease research.

b. High Computational Demands: Deep learning algorithms are computationally intensive and require high-performance computing resources to train the models. This can make it difficult to apply deep learning to Alzheimer's disease research, especially for smaller research groups.

c. Overfitting: Overfitting occurs when a deep learning model becomes too specialized to the training data, making it less effective at generalizing to new data. This is a common challenge in Alzheimer's disease research, as the number of labeled samples is often limited.

d. Interpretability: Deep learning algorithms are often difficult to interpret, making it challenging to understand the underlying mechanisms that lead to their predictions. This makes it difficult to validate the results and build trust in the predictions made by the model.

e. Dealing with Imbalanced Datasets: Alzheimer's disease datasets are often imbalanced, with a large number of healthy samples compared to Alzheimer's disease samples. This can make it difficult to train deep learning models, as the model may become biased towards the majority class.

These are some of the challenges faced by Alzheimer's disease researchers when using deep learning algorithms. Despite these challenges, deep learning has shown great potential in Alzheimer's disease research and is expected to make significant contributions in the future.

Giulia Liberati et al [27] focuses on developing a brain-computer interface (BCI) for Alzheimer's disease patients. The BCI combines classical conditioning and brain state classification to provide a way for Alzheimer's patients to communicate with the outside world. The goal of the BCI is to provide a non-invasive and effective method for Alzheimer's patients to communicate with their caregivers. The authors suggest that classical conditioning can be used to train Alzheimer's patients to associate certain stimuli with specific brain states. This information can then be used to classify brain states and determine the patient's intention. The authors also describe a system that combines classical conditioning and brain state classification to provide a BCI that is both effective and user-friendly.

The BCI is designed to be non-invasive, making it a safe and effective solution for Alzheimer's patients. The system is also designed to be adaptable, allowing it to be customized to the individual needs of each patient. This makes it a valuable tool for Alzheimer's patients, who often have trouble communicating with their caregivers. Overall, the paper provides a promising approach to developing a BCI for Alzheimer's patients, combining classical conditioning and brain state classification to provide a non-invasive and effective method for communication. The authors suggest using classical conditioning to train Alzheimer's patients to associate certain stimuli with specific brain states. This information can then be used to classify brain states and determine the patient's intention. For example, the authors describe a scenario in which an Alzheimer's patient is shown a picture of a cup and is asked to imagine holding a cup. This association between the picture and the imagination of holding the cup is then used to classify the patient's brain state and determine their intention to indicate a desire for a drink. By combining classical conditioning and brain state classification, the authors aim to create a brain-computer interface (BCI) that allows Alzheimer's patients to communicate their needs and desires to their caregivers. This BCI provides a non-invasive and user-friendly solution for Alzheimer's patients, who often have trouble communicating with others.

III. DISSCUSSIONS

Based on the publication "A Functional Model for Unifying Brain-Computer Interface Terminology" by Chuck Easttom,[5] the following points can be discussed:

1. The importance of standardizing terminology in BCI: Brain-computer interfaces are a rapidly developing field, with new technologies and applications being developed all the time. However, the lack of a standardized terminology can cause confusion and hinder communication and collaboration among researchers, developers, and users.
2. Easttom's functional model: Easttom's publication presents a functional model for BCI that provides a framework for organizing and standardizing the terminology used in the field. The model is based on a systems approach and provides a clear structure for describing the various components and concepts involved in BCI.
3. Benefits of the functional model: The use of a functional model can help to reduce confusion and misunderstandings, improve communication among stakeholders, and accelerate the development of new BCI technologies and applications. The model can also serve as a reference for researchers and developers as they work on new BCI projects.
4. Limitations and future work: While the functional model is a valuable tool for unifying BCI terminology, it is not without limitations. For example, it may not capture the full complexity of some BCI systems or may not account for the latest advances in the field. Further research and updates to the model may be necessary as BCI technology continues to evolve.

In conclusion, the publication "A Functional Model for Unifying Brain-Computer Interface Terminology" by Chuck Easttom provides a valuable contribution to the BCI field. By standardizing terminology, the functional model can help to improve communication, collaboration, and the development of new BCI technologies and applications.

The publication "A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces" by Berdakh Abibullaev[8] presents a study on the selection of deep learning models for P300-based brain-computer interfaces (BCI). The following points can be discussed based on the publication:

a. P300-based BCI: P300-based BCIs are a type of BCI that use EEG signals to detect specific patterns, known as P300 signals that are associated with certain mental states or events. This type of BCI is used for applications such as communication, control of assistive devices, and rehabilitation.

b. Deep Learning Models for P300-based BCI: The use of deep learning models has become increasingly popular for P300-based BCI due to their ability to learn complex patterns in EEG signals. However, selecting the right deep learning model for a given P300-based BCI application can be a challenge.

c. Abibullaev's Study: Abibullaev's publication presents a systematic study on the selection of deep learning models for P300-based BCI. The study evaluates several different deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), on a large dataset of EEG signals.

d. Findings: The study found that CNNs were generally more effective than RNNs for P300-based BCI. The study also found that certain deep learning models performed better for different types of EEG signals, suggesting that the choice of model may depend on the specific BCI application.

e. Implications: The results of Abibullaev's study have important implications for the development of P300-based BCI. By providing a systematic approach to deep learning model selection, the study can help researchers and developers choose the best models for their specific BCI applications.

In conclusion, the publication "A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces" by Berdakh Abibullaev provides a valuable contribution to the BCI field. The study's findings on the selection of deep learning models for P300-based BCI can help to improve the performance and reliability of these systems and enable new and more effective applications.

The publication "A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals" by Mahshad Ouchani[2]presents a review of methods for diagnosing and analyzing the complexity of Alzheimer's disease (AD) using EEG signals. The following points can be discussed based on the publication:

• Importance of EEG signals for AD Diagnosis: EEG signals have been used as a diagnostic tool for AD due to their ability to reflect changes in brain activity associated with the disease. The use of EEG signals for AD diagnosis is non-invasive and cost-effective, making it an attractive alternative to other diagnostic methods.
• Methods for EEG-based AD Diagnosis: Ouchani's publication reviews several methods for diagnosing AD using EEG signals, including spectral analysis, complexity analysis, and machine learning. These methods can provide information on changes in brain activity, such as changes in power, synchrony, and entropy, which are associated with AD.
• Complexity Analysis: Complexity analysis is a method that evaluates the complexity of EEG signals and can provide information on changes in brain activity associated with AD. Ouchani's publication reviews several complexity analysis methods, including entropy, fractal dimensions, and phase synchronization.
• Limitations and Future Work: While EEG-based AD diagnosis is a promising field, there are limitations to the methods reviewed in Ouchani's publication. For example, the methods may not be equally effective for all types of EEG signals, and there may be differences in performance depending on the stage of AD. Further research is needed to address these limitations and improve the accuracy and reliability of EEG-based AD diagnosis.

In conclusion, the publication "A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals" by Mahshad Ouchani provides a valuable overview of the methods used for diagnosing and analyzing the complexity of AD using EEG signals. The review highlights the potential of EEG-based AD diagnosis and the need for further research to improve the accuracy and reliability of these methods.

Conclusion

In conclusion, brain-computer interfaces (BCIs) have the potential to provide new communication avenues for individuals with Alzheimer\'s disease and improve their quality of life. However, the development and implementation of BCIs for this population pose several significant challenges, including issues related to data interpretation and translation, usability for individuals with cognitive decline, and long-term effectiveness. Despite these challenges, the potential of BCI technology to enhance communication in individuals with Alzheimer\'s disease cannot be ignored. Further research is needed to advance the field and overcome the challenges associated with BCI implementation in this population. Ethical and privacy concerns must also be addressed to ensure the responsible use of BCI technology for individuals with Alzheimer\'s disease. The review highlights the current state of BCI research for individuals with Alzheimer\'s disease and the need for continued investigation to realize the full potential of BCI technology for this population.

References

[1] Remigiusz J. Rak, Marcin Ko?odziej, Andrzej Majkowski“brain-computer interface as measurement and control system”Metrol. Meas. Syst., Vol. XIX (2012), No. 3, pp. 427-444. [2] Mahshad Ouchani “A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals” Hindawi BioMed Research International Volume 2021, Article ID 5425569, 15 pages https://doi.org/10.1155/2021/5425569 [3] R. Ducksbury, T. Whit_eld, and Z. Walker, ``SPECT/PET findings in Lewy body dementia,\'\' in PET and SPECT in Neurology. Berlin, Germany: Springer, 2014, pp. 373_415. [4] D. A. Wolk, Z. Zhang, S. Boudhar, C. M. Clark, M. J. Pontecorvo, and S. E. Arnold, ``Amyloid imaging in Alzheimer\'s disease: Comparison of _orbetapir and Pittsburgh compound-B positron emission tomography,\'\' J. Neurol., Neurosurg. Psychiatry, vol. 83, no. 9, pp. 923_926, Sep. 2012. [5] C. Easttom et al., \"A Functional Model for Unifying Brain Computer Interface Terminology,\" in IEEE Open Journal of Engineering in Medicine and Biology, vol. 2, pp. 91-96, 2021, doi: 10.1109/OJEMB.2021.3057471. [6] Dong-Kyun Han et al., “Domain Generalization for Session-Independent Brain-Computer Interface” in 2021 9th International Winter Conference on Brain-Computer Interface (BCI). [7] J. Fumanal-Idocin, Y. -K. Wang, C. -T. Lin, J. Fernández, J. A. Sanz and H. Bustince, \"Motor-Imagery-Based Brain–Computer Interface Using Signal Derivation and Aggregation Functions,\" in IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7944-7955, Aug. 2022, doi: 10.1109/TCYB.2021.3073210. [8] B. Abibullaev and A. Zollanvari, \"A Systematic Deep Learning Model Selection for P300-Based Brain–Computer Interfaces,\" in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2744-2756, May 2022, doi: 10.1109/TSMC.2021.3051136. [9] Lance, Brent & Kerick, Scott & Ries, Anthony & Oie, Kelvin & McDowell, Kaleb. (2012). Brain Computer Interface Technologies in the Coming Decades. Proceedings of the IEEE. 100. 10.1109/JPROC.2012.2184830. [10] Sheng-Ta Hsieh et al “A Brain Computer Interface for Attention Study” in 2019 in internaltional conference on robotics and control engineering. [11] Abdelkader Nasreddine Belkacem et al.,“Brain Computer Interfaces for Improving the Quality ofLifeofOlderAdultsandElderlyPatients”Front.Neurosci.,30June2020Sec.NeuralTechnologyVolume 14 – 2020. [12] Remigiusz J. Rak et al “Brain-Computer Interface As Measurement And Control System The Review Paper” Metrol. Meas. Syst., Vol. XIX (2012), No. 3, pp. 427-444. [13] D. Manzak, G. Çetinel and A. Manzak, \"Automated Classification of Alzheimer’s Disease using Deep Neural Network (DNN) by Random Forest Feature Elimination,\" 2019 14th International Conference on Computer Science & Education (ICCSE), Toronto, ON, Canada, 2019, pp. 1050-1053, doi: 10.1109/ICCSE.2019.8845325. [14] Sofia de la Fuente Garcia et al., “Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review” Journal of Alzheimer’s Disease 78 (2020) 1547–1574 DOI 10.3233/JAD-200888 IOS Press [15] Maria Luisa et al “Alzheimer’s disease and automatic speech analysis: a review, Expert Systems With Applications” (2020), Published by Elsevier Ltd. [16] Melanie Fried-Oken, et al “Brain-Computer Interface to Enhance Attention in Alzheimer’s disease” Supplement award to R01DC009834 National Institute on Deafness and Other Communication Disorders Oregon Health & Science University 3/1/2018-2/28/20217. [17] JAMAL F. HWAID et al., “Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach” ieee access May 9, 2022. Digital Object Identifier 10.1109/ACCESS.2022.3171906. [18] Lee T-S, Goh SJA, Quek SY, Phillips R, Guan C, et al. (2013) “A Brain-Computer Interface Based Cognitive Training System for Healthy Elderly: A Randomized Control Pilot Study for Usability and Preliminary Efficacy.”PLoS ONE 8(11): e79419. doi:10.1371/journal.pone.0079419. [19] Miltiadous, A.; Tzimourta, K.D.; Giannakeas, N.; Tsipouras, M.G.; Afrantou, T.; Ioannidis, P.; Tzallas, A.T. “Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods. Diagnostics” 2021, 11, 1437. https://doi.org/10.3390/diagnostics 11081437. [20] Sitara Afzal et al.,“A Data Augmentation based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection” DOI 10.1109/ACCESS.2019.2932786, IEEE Access. [21] Niago Moreira Nobre Leit et al “Deep Convolutional Autoencoder for EEG Noise Filtering” 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). [22] Yang Qui et al., “Denoising Sparse Autoencoder Based Ictal EEG Classification” DOI 10.1109/TNSRE.2018.2864306, IEEE. [23] HONGLI ZHANG et al., “Expression-EEG Based Collaborative Multimodal Emotion Recognition Using Deep AutoEncoder” September 21, 2020. Digital Object Identifier 10.1109/ACCESS.2020.3021994. [24] C. Jia, X. Gao, B. Hong and S. Gao*, \"Frequency and Phase Mixed Coding in SSVEP-Based Brain--Computer Interface,\" in IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 200-206, Jan. 2011, doi: 10.1109/TBME.2010.2068571. [25] Rajdeep Ghosh et al “Automated eye blink artefact removal from EEG using support vector machine and autoencoder” ISSN 1751-9675 Received on 1st May 2018 Revised 7th July 2018 Accepted on 13th August 2018 E-First on 2nd November 2018 doi: 10.1049/iet-spr.2018.5111 www.ietdl.org. [26] Hoang Nguyen et al “An Introduction to Deep Learning Research for Alzheimer’s Disease” June 15,2021 at 12:09:09 UTC from IEEE Xplore. [27] Liberati, Giulia & Dalboni, Josue & van der Heiden, Linda & Raffone, Antonino & Birbaumer, Niels & Belardinelli, Marta & Sitaram, Ranganatha. (2012). “Toward a Brain-Computer Interface for Alzheimer\'s Disease Patients by Combining Classical Conditioning and Brain State Classification. Journal of Alzheimer\'s disease” JAD. 31. S211-20. 10.3233/JAD-2012-112129. [28] Ehrlich, S.; Guan, C.; Cheng, G. A closed-loop brain-computer music interface for continuous affective interaction. In Proceedings of the 2017 International Conference on Orange Technologies (ICOT), Singapore, 8–10 September 2017; pp. 176–179. [29] Placidi, G.; Cinque, L.; Di Giamberardino, P.; Iacoviello, D.; Spezialetti, M. An affective BCI driven by self-induced emotions for people with severe neurological disorders. In International Conference on Image Analysis and Processing; Springer: Berlin/Heidelberg, Germany, 2017; pp. 155–162. [30] Sheng-Ta Hsieh Department of Communication Engineering Oriental Institute of Technology New Taipei City, Taiwan, Chun-Ling Lin Department of Electrical Engineering Ming Chi University of Technology New Taipei City, Taiwan “A Brain Computer Interface for Attention Study”I the iteratioal coferece of itelliget rootic ad control egieerig i 2019(missing correct) [31] Dhanush Rajagopal, Hemanth S R, Yashaswini N, Sachin M K, & Suryakanth M N. (2020). Detection of Alzheimer’s disease using BCI. International Journal of Progressive Research in Science and Engineering, 1(4), 184–190. Retrieved fromhttps://www.journals.grdpublications.com/index.php/ijprse/ticle/view/127

Copyright

Copyright © 2023 Aathira R Kurup, Dr. Baulkani S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.