1D-CNN architectures for EEG classification with motor imagery input of eyes open and eyes closed conditions. (19th November 2021)

• Record Type: Journal Article
• Authors: Nagabushanam, P.; George, S. Thomas; Subathra, M.S.P.; Radha, S.
• Is Part Of: International journal of intelligence and sustainable computing. Volume 1:Number 3(2021)
• Page Start: 280
• Page End: 298
• Publication Date: 2021-11-19
• Subjects: 1D-CNN -- motor imagery EEG -- classification -- trainable parameters -- convolutional layers; Computer science -- Periodicals; Computational intelligence -- Periodicals; Computer networks -- Environmental aspects -- Periodicals; 004
• Languages: English
• ISSNs: 2517-763X

Abstract

Neuro cognitive performance is an interesting area which deals with normal and abnormal conditions of a person in his thinking capabilities like driving, reaction to the events happening around him. The study of such information is called sleep scoring or sleep stages classification. If it is abnormal for a person, it further leads to insomnia, hypersomnia, epilepsy disorders. Hence, early stage detection of such abnormalities may help in treating the person in time and avoiding major problems ahead. Sleep stage classification may be 2-way, 3-way, 4-way or 5-way based on type of EEG captured from the person. In this paper, we have proposed various 1D-CNN architectures for 2-way sleep stage classification for motor imagery EEG captured during eyes open and eyes closed conditions. Implementation is done in python keras in Jupiter notebook, anaconda. Among the proposed models, 1D-CNN with four layers give better accuracy of 75.753%. In general, sleep stages classification give accuracy less than 80% and also it may be based on quality of EEG input considered.