EEG differentiates left and right imagined Lower Limb movement
Autor: | Adrienne Kline, Calin Gaina Ghiroaga, Daniel J. Pittman, Janet L. Ronsky, Bradley G. Goodyear |
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Rok vydání: | 2021 |
Předmět: |
Adult
Male Left and right medicine.medical_specialty Supine position Movement Biophysics Electroencephalography Lower limb Young Adult 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation medicine Humans Orthopedics and Sports Medicine Brain–computer interface medicine.diagnostic_test Movement (music) Rehabilitation Signal Processing Computer-Assisted 030229 sport sciences Healthy Volunteers Lower Extremity Right lower limb Eeg electrodes Psychology 030217 neurology & neurosurgery |
Zdroj: | Gait & Posture. 84:148-154 |
ISSN: | 0966-6362 |
DOI: | 10.1016/j.gaitpost.2020.11.014 |
Popis: | Background Identifying which EEG signals distinguish left from right leg movements in imagined lower limb movement is crucial to building an effective and efficient brain-computer interface (BCI). Past findings on this issue have been mixed, partly due to the difficulty in collecting and isolating the relevant information. The purpose of this study was to contribute to this new and important literature. Research Question Can left versus right imagined stepping be differentiated using the alpha, beta, and gamma frequencies of EEG data at four electrodes (C1, C2, PO3, and PO4)? Methods An experiment was conducted with a sample of 16 healthy male participants. They imagined left and right lower limb movements across 60 trials at two time periods separated by one week. Participants were fitted with a 64-electrode headcap, lay supine on a specially designed device and then completed the imagined task while observing a customized computer-generated image of a human walking to signify the left and right steps, respectively. Results Findings showed that eight of the twelve frequency bands from 4 EEG electrodes were significant in differentiating imagined left from right lower limb movement. Using these data points, a neural network analysis resulted in an overall participant average test classification accuracy of left versus right movements at 63 %. Significance Our study provides support for using the alpha, beta and gamma frequency bands at the sensorimotor areas (C1 and C2 electrodes) and incorporating information from the parietal/occipital lobes (PO3 and PO4 electrodes) for focused, real-time EEG signal processing to assist in creating a BCI for those with lower limb compromised mobility. |
Databáze: | OpenAIRE |
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