An EEG pre-processing technique for the fast recognition of motor imagery movements

Abstract

In this paper we propose a new pre-processing technique οf Electroencephalography (EEG) signals produced by motor imagery movements. This technique results to an accelerated determination of the imagery movement and the command to carry it out, within the time limits imposed by the requirements of brain-based real-time control of rehabilitation devices, making thus feasible to drive these devices according to patient's will. Based on event related desynchronization and synchronization (ERD/ERS) of motor imagery, the received patient signal is first subjected to the removal of environmental, system and interference noise which correspond to normal human activities such as eye-blinking and cardiac motion. Next, power and energy features of the processed signal are compared with the same features of classified signals from an available database and the class to which the processed signal belongs, is identified. The database classification is done off-line by using the SVM algorithm.

Reward and expectancy effects on neural signals of motor preparation and execution

“…To determine the fine-grained temporal cascade of LRPs, we identified the LRP in both the stimulus-locked ( sl LRP) and response-locked ( rl LRP) data. We focused on the following cortical motor regions of interest (ROI) on each side of the hemisphere: left motor (FC3, FC5, C3, C5), right motor (FC4, FC6, C4, C6) (Deiber et al, 2012;Gregory, George, & George, 2016;L opez-Larraz, Montesano, Gil-Agudo, Minguez, & Oliviero, 2015;Picazio et al, 2014). Lateralized motor signals were identified by contrasting the ERPs of the ipsilateral from contralateral motor ROI relative to the hand that was used to respond.…”

Reward and expectancy effects on neural signals of motor preparation and execution

“…To determine the fine-grained temporal cascade of LRPs, we identified the LRP in both the stimulus-locked and response-locked data. We focused on the following cortical motor regions of interest (ROI) on each side of the hemisphere: left motor (FC3, FC5, C3, C5), right motor (FC4, FC6, C4, C6) (Deiber et al, 2012;Gregory et al, 2016;López-Larraz et al, 2015;Picazio et al, 2014). Lateralized motor signals were identified by contrasting the ERPs of the ipsilateral from contralateral motor ROI relative to the hand that was used to respond (Fig.…”

General model for best feature extraction of EEG using discrete wavelet transform wavelet family and differential evolution

“…There is no doubt that preprocessing is an important and essential step for any system based on the signal. Several recent studies have tried to find a better method to process EEG signals like the study that was presented by Gregory et al 10 They proposed a new approach to process (EEG) signals. They utilized two methods, which are: event-related de-synchronization (ERD) technique and event-related synchronization (ERS) technique.…”