Identification of Brain Electrical Activity Related to Head Yaw Rotations

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 10; p. 3345
• Main Authors: Zero, Enrico, Bersani, Chiara, Sacile, Roberto
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 11.05.2021; MDPI
• Subjects: Algorithms; brain electrical activity; Brain research; brain-computer interface; Classification; Discriminant analysis; Electrodes; Electroencephalography; Experiments; feedforward neural networks; Lamps; Neural networks; Support vector machines; system identification; Wheelchairs
• DOI: 10.3390/s21103345
• ISSN: 1424-8220

Automatizing the identification of human brain stimuli during head movements could lead towards a significant step forward for human computer interaction (HCI), with important applications for severely impaired people and for robotics. In this paper, a neural network-based identification technique is presented to recognize, by EEG signals, the participant’s head yaw rotations when they are subjected to visual stimulus. The goal is to identify an input-output function between the brain electrical activity and the head movement triggered by switching on/off a light on the participant’s left/right hand side. This identification process is based on “Levenberg–Marquardt” backpropagation algorithm. The results obtained on ten participants, spanning more than two hours of experiments, show the ability of the proposed approach in identifying the brain electrical stimulus associate with head turning. A first analysis is computed to the EEG signals associated to each experiment for each participant. The accuracy of prediction is demonstrated by a significant correlation between training and test trials of the same file, which, in the best case, reaches value r = 0.98 with MSE = 0.02. In a second analysis, the input output function trained on the EEG signals of one participant is tested on the EEG signals by other participants. In this case, the low correlation coefficient values demonstrated that the classifier performances decreases when it is trained and tested on different subjects.