Deep learning EEG response representation for brain computer interface

Liu, Jingwei; Cheng, Yin; Zhang, Wensheng

doi:10.1109/chicc.2015.7260182

Cited by 25 publications

(9 citation statements)

References 20 publications

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“…EEG, which is one of these devices, is more preferred than other tools because it is noninvasive, economical, practical, and easy to operate. The accuracy, robustness, and reliability of the EEGrelated methodology combined with deep learning (DL) has proven with many research in brain-computer interface (BCI), especially in motor imagery task classification, [5][6][7][8][9][10][11][12][13] epileptic seizure prediction and detection, [14][15][16][17][18][19][20][21][22][23] drivers fatigue prediction, 24,25 emotion and affective state classification, [26][27][28][29][30][31][32] sleep stage detection, [33][34][35][36] prognosis in rapid eye movement behavior disorder, 37 EEG-based diagnosis of various neurodegenerative diseases, including attention deficit/hyperactivity disorder, 38 schizophrenia, 39,40 Creutzfeldt-Jacob disease, 41 Parkinson's disease, 42 Alzheimer's disease, 43 mild cognitive impairment, 44 predicting transcranial direct current stimulation treatment outcomes of patients with MDD has been studied in the recent literature. 45 The performance of a machine learning methodology based on the pretreatment EEG for MDD pro...…”

Section: Introductionmentioning

confidence: 99%

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

et al. 2020

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The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

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Section: Introductionmentioning

confidence: 99%

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

et al. 2020

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“…CNN is widely used for the recognition of MI EEG [245]. On the one hand, some studies CNN is only used as a classi er to recognize manually extracted features [86,232]. Uktveris et al [210] extracted a large number of EEG features including Mean channel energy (MCE), Mean window energy (MWE), Channel variance (CV), Mean band power (BP), etc.…”

Section: Eeg Oscillatorymentioning

confidence: 99%

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Zhang

Yao

Wang

et al. 2019

Preprint

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Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has enhanced the performance of brain-computer interface systems signi cantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions, most published in the past ve years. Finally, we discuss the applied areas, emerging challenges, and future directions for deep learning-based BCI.

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“…Recently, several EEG studies have adopted deep learning algorithms [42][43][44][45][46]. Liu et al applied a convolutional neural network (CNN) to motor imagery tasks [25]. Hajinoroozi et al tried to predict drivers' cognitive states (drowsy or alert) with a channel-wise convolutional neural network (CCNN) [26].…”

Section: Introductionmentioning

confidence: 99%

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks

Lin

Chuang

Hung

et al. 2021

IEEE Trans. Cybern.

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Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal and 2D spatial information, of EEG channel location were comprehensively considered. A 4D convolutional neural network (4D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. 4D CNN achieves superior forecasting performance over 2D CNN, 3D CNN and shallow networks. The results showed a 3.82% improvement in the RMSE, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4D CNN compared with 3D CNN. The 4D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.

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Deep learning EEG response representation for brain computer interface

Cited by 25 publications

References 20 publications

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks

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