Brain computer interface (BCI) is the current trend in technology expansion as it provides an easy interface between human brain and machine. The demand for BCI based applications is growing tremendously and efforts are in progress to deploy BCI devices for real world applications. One of the widely known applications of BCI technology is rehabilitation in which BCI devices can provide various types of assistance to specially-abled persons. In this paper the effect of hand actions on objects is analyzed for motor related mental task. The proposed approach analysis electroencephalogram (EEG) based brain activity which was captured for images shown with different gripping actions on objects. The EEG recordings are first pre-processed, followed by extraction of epochs and frequency bands using discrete wavelet transform (DWT), afterwards feature extraction followed by training and classification steps are performed for classifying the grip action into congruent (correct) and incongruent (incorrect) grip categories. The proposed work makes use of average power and relative wavelet energy as discriminating features which are then fed to train an artificial neural network for automatically classifying the incoming EEG patterns into correct or incorrect object hand grips. The performance evaluation of proposed system is done on real EEG data set obtained from 14 subjects. Experimental results have shown an accuracy of 75%. Also, to evaluate the effectiveness of our work, a comparison of our work with other state of art works reported by different authors is presented at the end. The results show the effectiveness of proposed approach and suggest further that the system can be used for analyse and train subjects having motor-related disabilities for perceiving correct or incorrect hand grips on objects.
Mishra, AnjuSharma, ShanuKumar, SanjayRanjan, PriyaUjlayan, Amit
Department of Psychology, Health and Professional Development
Year of publication: 2020Date of RADAR deposit: 2020-06-16
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