Classification of Relaxation and Concentration Mental States with EEG

• Published in: Information (Basel) Vol. 12; no. 5; p. 187
• Main Author: You, Shingchern D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2021
• Subjects: Accuracy; Classification; Classifiers; Creutzfeldt-Jakob disease; EEG; Electroencephalography; Experiments; Illiteracy; Machine learning; Man-machine interfaces; Meditation; mental states; Multilayers; Musical performances; neural networks; Support vector machines; SVM; Usability
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info12050187

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.