SwePub - sökning: WFRF:(Kotikawatte Thivra)

Numrering	Referens	Omslagsbild	Hitta
1.	Augustian, Midhumol, et al. (författare) EEG Analysis from Motor Imagery to Control a Forestry Crane 2018 Ingår i: Intelligent Human Systems Integration (IHSI 2018). - Cham : Springer. - 9783319738871 - 9783319738888 ; , s. 281-286 Konferensbidrag (refereegranskat)abstract Brain-computer interface (BCI) systems can provide people with ability to communicate and control real world systems using neural activities. Therefore, it makes sense to develop an assistive framework for command and control of a future robotic system which can assist the human robot collaboration. In this paper, we have employed electroencephalographic (EEG) signals recorded by electrodes placed over the scalp. The human-hand movement based motor imagery mentalization is used to collect brain signals over the motor cortex area. The collected µ-wave (8–13 Hz) EEG signals were analyzed with event-related desynchronization/synchronization (ERD/ERS) quantification to extract a threshold between hand grip and release movement and this information can be used to control forestry crane grasping and release functionality. The experiment was performed with four healthy persons to demonstrate the proof-of concept BCI system. From this study, it is demonstrated that the proposed method has potential to assist the manual operation of crane operators performing advanced task with heavy cognitive work load.

Augustian, Midhumol, et al. (författare)
EEG Analysis from Motor Imagery to Control a Forestry Crane
2018
Ingår i: Intelligent Human Systems Integration (IHSI 2018). - Cham : Springer. - 9783319738871 - 9783319738888 ; , s. 281-286
Konferensbidrag (refereegranskat)abstract
- Brain-computer interface (BCI) systems can provide people with ability to communicate and control real world systems using neural activities. Therefore, it makes sense to develop an assistive framework for command and control of a future robotic system which can assist the human robot collaboration. In this paper, we have employed electroencephalographic (EEG) signals recorded by electrodes placed over the scalp. The human-hand movement based motor imagery mentalization is used to collect brain signals over the motor cortex area. The collected µ-wave (8–13 Hz) EEG signals were analyzed with event-related desynchronization/synchronization (ERD/ERS) quantification to extract a threshold between hand grip and release movement and this information can be used to control forestry crane grasping and release functionality. The experiment was performed with four healthy persons to demonstrate the proof-of concept BCI system. From this study, it is demonstrated that the proposed method has potential to assist the manual operation of crane operators performing advanced task with heavy cognitive work load.

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