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A brain-computer interface (BCI) based on electroencephalography (EEG) can beuseful for rehabilitation and the control of external devices. Five graspingtasks were decoded for motor execution (ME) and motor imagery (MI). During thisexperiment, eight healthy subjects were asked to imagine and grasp fiveobjects. Analysis of EEG signals was performed after detecting muscle signalson electromyograms (EMG) with a time interval selection technique on data takenfrom these ME and MI experiments. By refining only data corresponding to theexact time when the users performed the motor intention, the proposed methodcan train the decoding model using only the EEG data generated by various motorintentions with strong correlation with a specific class. There was an accuracyof 70.73% for ME and 47.95% for MI for the five offline tasks. This method maybe applied to future applications, such as controlling robot hands with BCIs.

Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset