Learned EEG-based brain self-regulation of motor-related oscillations during application of transcranial electric brain stimulation: feasibility and limitations

Abstract

Objective: Transcranial direct current stimulation (tDCS) improves motor learning and can affect emotional processing and attention. However, it is unclear whether learned electroencephalography (EEG)-based brain-machine interface (BMI) control during tDCS is feasible, how application of transcranial electric currents during BMI control would interfere with feature-extraction of physiological brain signals and how it affects brain control performance. Here we tested this combination and evaluated stimulation-dependent artifacts across different EEG frequencies and stability of motor imagery-based BMI control.

Approach: Ten healthy volunteers were invited to two BMI-sessions, each comprising two 60-trial blocks. During the trials, learned desynchronization of mu-rhythms (8-15 Hz) associated with motor imagery (MI) recorded over C4 was translated into online cursor movements on a computer screen. During block 2, either sham (session A) or anodal tDCS (session B) was applied at 1 mA with the stimulation electrode placed 1 cm anterior of C4.

Main results: tDCS was associated with a significant signal power increase in the lower frequencies most evident in the signal spectrum of the EEG channel closest to the stimulation electrode. Stimulation-dependent signal power increase exhibited a decay of 12 dB per decade, leaving frequencies above 9 Hz unaffected. Analysis of BMI control performance did not indicate a difference between blocks and tDCS conditions.

Conclusion: Application of tDCS during learned EEG-based self-regulation of brain oscillations above 9 Hz is feasible and safe, and might improve applicability of BMI systems.

Keywords: EEG; brain-machine interface (BMI) control; motor imagery; stimulation artifacts; transcranial electric stimulation (TES).

Figure 1

(A) Experimental setup for simultaneous transcranial electric current stimulation during electroencephalography (EEG). The active stimulation electrode was placed immediately anterior to the EEG electrode C4 used for online brain-machine interface (BMI) control (right yellow circle). The reference stimulation electrode was placed over the left supraorbital region (blue). (B) BMI paradigm. Electric brain activity recorded at electrode C4 was translated into visual feedback. Event-related desynchronization of mu-rhythms (9–15 Hz, mu-ERD) was indicated by upward movements of a blue ball, while mu-event-related synchronization was indicated by downward movements (mu-ERS). Participants were instructed to keep the ball above the dotted horizontal line during the task to hit the target (indicated by red bar).

Figure 2

Power spectra of the electroencephalographic (EEG) signals recorded from electrode C4 (A) indicated by yellow circles in the upper panels and P3 (B) during sham stimulation (left column, red curve) and anodal stimulation (right column, red curve). Power spectra of trials in absence of stimulation (block 1) are shown in blue. While not significant during sham stimulation (left column), anodal stimulation resulted in significant signal changes in delta (0–4 Hz) and theta (4–9 Hz) oscillations at electrode position C4 (indicated by the gray underlay in the upper right panel), equally present in delta, and in trend in theta oscillations recorded at P3 (indicated by the gray underlay in the lower right panel), while alpha (9–15 Hz) and beta (15–30 Hz) frequencies showed no difference between conditions.

Figure 3

Time-frequency representation (TFR) of brain oscillations recorded from EEG electrode position C4 at 1 cm distance from the active electric brain stimulation electrode. (A) Block 1 of session A (left) and session B (right) in absence of electric brain stimulation. (B) Block 2 of session A (left, during sham stimulation) and session B (right, during anodal stimulation). Note the signal power increase in frequencies below 9 Hz in block 2 of session B (during anodal stimulation) across task-free and task intervals. (C) Signal power differences between block 1 and block 2 are plotted separately for both sessions (session A: left graph; session B: right graph), indicating no significant stimulation-dependent signal changes above 9 Hz.

Figure 4

Motor imagery (mu-event-related desynchronization, mu-ERD)-based brain-machine interface (BMI) performance was defined as the percentage of time mu-ERD was detected during trials. BMI performance was comparable between both sessions and did not exhibit differences between block 1 and block 2. There was neither a difference between block 1 of session A and session B (p = 0.541), nor a difference between block 1 and block 2 of session A (p = 0.880) or session B (p = 0.470).