Performance of Motor Imagery Brain-Computer Interface Based on Anodal Transcranial Direct Current Stimulation Modulation

Abstract

Abstract-Voluntarily modulating neural activity plays a key role in brain-computer interface (BCI). In general, the self-regulated neural activation patterns are used in the current BCI systems involving the repetitive trainings with feedback for an attempt to achieve a high-quality control performance. With the limitation posed by the training procedure in most BCI studies, the present work aims to investigate whether directly modulating the neural activity by using an external method could facilitate the BCI control. We designed an experimental paradigm that combines anodal transcranial direct current stimulation (tDCS) with a motor imagery (MI)-based feedback EEG BCI system. Thirty-two young and healthy human subjects were randomly assigned to the real and sham stimulation groups to evaluate the effect of tDCS-induced EEG pattern changes on BCI classification accuracy. Results showed that the anodal tDCS obviously induces sensorimotor rhythm (SMR)-related event-related desynchronization (ERD) pattern changes in the upper-mu (10-14 Hz) and beta (14-26 Hz) rhythm components. Both the online and offline BCI classification results demonstrate that the enhancing ERD patterns could conditionally improve BCI performance. This pilot study suggests that the tDCS is a promising method to help the users to develop reliable BCI control strategy in a relatively short time.Index Terms-Brain-computer interface (BCI), motor imagery, neuro-modulation, transcranial direct current stimulation (tDCS).

Evidence that transcranial direct current stimulation (tDCS) generates little-to-no reliable neurophysiologic effect beyond MEP amplitude modulation in healthy human subjects: A systematic review

“…Dieckhofer et al (2006) 1 0 9 A ,C -CP3 ? T0, T20 Kirimoto et al (2011/x2) 10 (x2) 15 (x2) A, C, S (x1)A, C (x1) -CP3 300 T0, T15À 20 N2 and P2 LEP amplitude Antal et al (2008) 1 0 1 5 A , C , S -Cz 40 T0 Terney et al (2008) 12 A,C, 7 S 15 C, S -Cz 40 T0 Csifcsak et al (2009) 1 0 1 0 A ,C ,S -Cz 40 T0 Mu/alpha ERD: motor imagery Matsumoto et al (2010) 6 10 A, C, S -C3 and surrounds n/a T0 Wei et al (2013) 8 A, 8 S 15 A, S -C3 and surrounds n/a T0 Lapenta et al (2013) 2 1 2 0 A ,C ,S -C3 and surrounds n/a T0…”

Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance

“…Previous studies did not report the effect of tDCS on BCI task timing as timing was not variable [32,33]. For motor learning tasks examining the speed of performance, Nitsche et al [23] used a serial reaction time task with left M1 stimulation and right hand movement and found anodal subjects to have a decreased reaction time compared to cathodal and sham stimulation.…”

“…Previous electrophysiological studies found mixed effects on the ERD following tDCS stimulation. Initial MI work found an increase in ERD of the stimulated hemisphere (left M1) when performing MI [28,29] and MI-BCI [32] whereas others found no change in the ERD for MI, MI-BCI, or motor observation [30,33]. In our previous work [31], we found a decrease in beta ERD in the sensorimotor cortex of the stimulated hemisphere following HD-tDCS anodal stimulation as well as a trend towards increased ERD with cathodal stimulation, which we find here as well.…”

“…In addition to the effect of tDCS on motor imagery, there have been multiple studies examining MI-BCI performance with tDCS. An initial study of anodal tDCS prior to BCI performance found an increased ERD over the stimulated motor cortex during BCI performance of contralateral hand motor imagination following stimulation, but no change in performance within a single session [32]. Using trained subjects, Soekadar and colleagues found no change in performance within a session for those who received anodal stimulation compared to the sham group [33], however over multiple sessions with untrained subjects they found an increase in ERD following anodal stimulation [34].…”

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

“…Recent research suggests that users can learn to modulate their sensorimotor rhythm to issue control commands to physical devices [14]. Neural signals recorded using invasive, or to a limited degree, non-invasive, sense electronics can be processed for intent detection, and the outcome can be used for controlling external devices.…”