Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal

Islam, Mohammed Saidul; Hussain, Iqram; Rahman, Md Mezbaur; Park, Se Jin; Hossain, Md Azam

doi:10.3390/s22249859

Cited by 79 publications

(34 citation statements)

References 34 publications

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“…Our literature review included studies on EEG-NFB interventions in different neurological diseases, such as dementia, multiple sclerosis, strokes and traumatic brain injury. There are studies in which EEG signal is used as a potential predictive tool not only in neurological disorders and rehabilitation but also in sections of daily lives such as sleep stages and driving performance [ 30 , 31 , 32 ]. In our review, we emphasized the use of EEG signal as a therapeutic tool for cognitive deficits.…”

Section: Discussionmentioning

confidence: 99%

EEG-Neurofeedback as a Potential Therapeutic Approach for Cognitive Deficits in Patients with Dementia, Multiple Sclerosis, Stroke and Traumatic Brain Injury

Vilou

Varka

Parisis

et al. 2023

Life

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Memory deficits are common in patients with dementia, such as Alzheimer’s disease, but also in patients with other neurological and psychiatric disorders, such as brain injury, multiple sclerosis, ischemic stroke and schizophrenia. Memory loss affects patients’ functionality and, by extension, their quality of life. Non-invasive brain training methods, such as EEG neurofeedback, are used to address cognitive deficits and behavioral changes in dementia and other neurological disorders by training patients to alter their brain activity via operant activity. In this review paper, we analyze various protocols of EEG neurofeedback in memory rehabilitation in patients with dementia, multiple sclerosis, strokes and traumatic brain injury. The results from the studies show the effectiveness of the ΕΕG-NFB method in improving at least one cognitive domain, regardless of the number of sessions or the type of protocol applied. In future research, it is important to address methodological weaknesses in the application of the method, its long-term effects as well as ethical issues.

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Section: Discussionmentioning

confidence: 99%

EEG-Neurofeedback as a Potential Therapeutic Approach for Cognitive Deficits in Patients with Dementia, Multiple Sclerosis, Stroke and Traumatic Brain Injury

Vilou

Varka

Parisis

et al. 2023

Life

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“…The association between EEG indicators and neurologic prognosis following ischemic stroke has been studied in many EEG studies conducted in medical and healthcare settings. For example, in [ 25 ], they used machine learning (ML) algorithms on EEG signals to classify stroke patients and healthy people. In [ 26 ], they tried to categorize stroke patients’ and healthy adults’ electrical activity by collecting ambulatory EEG data.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Hashem

AbdulAzeem

Labib

et al. 2023

Sensors

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Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

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“…In recent years, machine learning (ML) models have been applied to EEG signals with explainable AI to develop prediction models for diseases such as stroke and to evaluate mental workload. Examples of such applications include based stroke prediction [ 11 , 12 ], assessment of task-induced neurological outcomes after stroke [ 13 ], detection of driving-induced neurological biomarkers [ 14 ], and prediction of sleep stages based on EEG biomarkers [ 15 ]. In emotion recognition, each emotion combines valence (a spectrum of negative to positive emotions) and arousal (the intensity) associated with the nervous system [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures

Tripathi

Sharma

2023

Sensors

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Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market prices. As such, their emotional state can greatly influence their decision-making, leading to suboptimal outcomes in volatile market conditions. Despite the use of risk control measures such as stop loss and limit orders, it is unclear if these strategies have a substantial impact on the emotional state of traders. In this paper, we aim to determine if the use of limit orders and stop loss has a significant impact on the emotional state of traders compared to when these risk control measures are not applied. The paper provides a technical framework for valence-arousal classification in financial trading using EEG data and deep learning algorithms. We conducted two experiments: the first experiment employed predetermined stop loss and limit orders to lock in profit and risk objectives, while the second experiment did not employ limit orders or stop losses. We also proposed a novel hybrid neural architecture that integrates a Conditional Random Field with a CNN-BiLSTM model and employs Bayesian Optimization to systematically determine the optimal hyperparameters. The best model in the framework obtained classification accuracies of 85.65% and 85.05% in the two experiments, outperforming previous studies. Results indicate that the emotions associated with Low Valence and High Arousal, such as fear and worry, were more prevalent in the second experiment. The emotions associated with High Valence and High Arousal, such as hope, were more prevalent in the first experiment employing limit orders and stop loss. In contrast, High Valence and Low Arousal (calmness) emotions were most prominent in the control group which did not engage in trading activities. Our results demonstrate the efficacy of our proposed framework for emotion classification in financial trading and aid in the risk-related decision-making abilities of day traders. Further, we present the limitations of the current work and directions for future research.

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Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal

Cited by 79 publications

References 34 publications

EEG-Neurofeedback as a Potential Therapeutic Approach for Cognitive Deficits in Patients with Dementia, Multiple Sclerosis, Stroke and Traumatic Brain Injury

EEG-Neurofeedback as a Potential Therapeutic Approach for Cognitive Deficits in Patients with Dementia, Multiple Sclerosis, Stroke and Traumatic Brain Injury

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures

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