Deep Learning Techniques for EEG Signal Applications – A Review

ABSTRACT

Electroencephalogram (EEG) can track the brain waves which contain the neural activity of the brain. EEG signals help to understand the physiological and functional details and activities of the brain. In the era of Artificial Intelligence (AI), machine learning algorithms were useful in brain disorder detection and classification. Recently, a rapid increase in using Deep Learning (DL) methods in various applications in EEG signals not only helps in the detection of brain disorders but also facilitates the recognition of human emotions and various psycho-neuro disorders. In order to offer a beneficial and broad perspective, a detailed survey on the application of deep learning architecture in EEG signals has been carried out in this paper. Different deep learning methods, using varied architecture in EEG signal analysis, offer an understanding to develop the next level of AI-based systems. This review will provide information about how deep learning methods are used in EEG signals and the challenges and limitations of each method in classification; moreover making it helpful for those who are exploring EEG signals using DL algorithms.

KEYWORDS:

• Deep learning algorithm
• Artificial neural network
• Electroencephalogram (EEG) signals
• Convolution neural network

Additional information

Notes on contributors

D. Merlin Praveena

D Merlin Praveena received the BTech degree in electronics and communication engineering from Karunya Institute of Technology and Sciences, Coimbatore, India in 2018. She is pursuing MTech degree in communication systems at Karunya Institute of Technology and Sciences, Coimbatore, India. Email: [email protected]

D. Angelin Sarah

D Angelin Sarah received the BTech degree in electronics and communication engineering from Karunya Institute of Technology and Sciences, Coimbatore, India in 2018. She is pursuing MTech degree in communication systems at Karunya Institute of Technology and Sciences, Coimbatore, India. Email: [email protected]

S. Thomas George

S Thomas George is a doctorate in the area of biosignal processing. He completed his BE and ME from Bharathiar University and PhD from Karunya University. He has been working in Karunya Institute of Technology and Sciences (Deemed to be University) for the past 21 years and 6 months and has acquired good teaching and research experience. To his credit, he has publications in reputed international journals and in a number of conference proceedings. His areas of interests are biosignal processing, image processing and optimization techniques. He is currently working as an associate professor in instrumentation engineering, Karunya Institute of Technology and Sciences, Coimbatore, India.