Various technological approaches have been developed in order to help those people who are unfortunateenough to be afflicted with different types of paralysis which limit them in performing their daily life activitiesindependently. One of the proposed technologies is the Brain-Computer Interface (BCI). The BCI system uses electroencephalography (EEG) which is generated by the subjectâs mental activityas input, and converts it into commands. Some previous experiments have shown the capability of the BCI system to predict the movement intention before the actual movement is onset. Thus research has predicted the movement by discriminating between data in the ârestâ condition, wherethere is no movement intention, with âpre-movementâ condition, where movement intention is detected before actual movement occurs. This experiment, however, was done to analyze the system for which machine learning was applied to data obtained in a continuous time interval, between 3 seconds before the movement was detected until 1 second after the actual movement was onset. This experiment shows that the system can discriminate the âpre-movementâ condition and ârestâ condition by using the EEG signal in 7-30 Hzwhere the Mu and Beta rhythm can be discovered with an average True Positive Rate (TPR) value of 0.64 Â± 0.11 and an average False Positive Rate (FPR) of 0.17 Â± 0.08. This experiment also shows that by using EEG signals obtained nearing the movement onset, the system has higher TPR or a detection rate in predicting the movement intention.
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