A brain-computer interface driven by imagining different force loads on a single hand: an online feasibility study

Abstract

BackgroundMotor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.MethodsEleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.ResultsAll subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution.ConclusionsThis paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.

Brain-Computer Interface Channel-Selection Strategy Based on Analysis of Event-Related Desynchronization Topography in Stroke Patients

“…The 32nd channel EOG was excluded from the EEG data. The raw data were referenced using an average reference [8, 23]. Then, the data were filtered using a finite impulse response (FIR) bandpass filter (1–40 Hz), and the baseline was removed.…”

“…Recent studies have demonstrated that electroencephalogram- (EEG-) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients [4–6], which is hypothesized that closing the loop between cortical activity (imagined or attempted motor intention) and actual movement can restore functional corticospinal and corticomuscular connections [7]. In applications with EEG-based BCI for healthy subjects, motor intention of the unilateral arm or hand can be indicated by the decline in the power of the sensorimotor rhythm, such as event-related desynchronization (ERD) [8–10], in the contralateral sensorimotor cortex (SM1) [11]. Similarly, in stroke rehabilitation with EEG-based BCI, SM1 is chosen to detect ERD [12–15].…”

Small-Dimension Feature Matrix Construction Method for Decoding Repetitive Finger Movements From Electroencephalogram Signals

“…The EEG recordings were preprocessed using EEGLAB 14.1.2b (EEGLAB toolbox, Swartz Center for Computational Neurosciences, La Jolla, CA; https://sccn.ucsd.edu/eeglab). The dataset excluded the 32nd channel EOG and then referenced using an average reference [38], [39]. Then the data was filtered using a finite impulse response (FIR) band pass filter (1-40Hz) with 1650 filter order as well as the removal of the baseline.…”

Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces

“…However, the signals that can be used for active BCI are still underdeveloped with a lack of variety. Only a few cognitive EEG features have been utilized, including the motor-induced phase-unlocked EEG feature, such as the event-related desynchronization (ERD) [ 12 , 13 ]; the neural modulation of covert attention on sensory ERPs, such as the enhanced visual N1/P3 [ 14 ]; and error detection ERPs, such as error-related potential (ErrP) [ 15 , 16 ]. As a result, the brain intentions that can be decoded are limited, restricting the development of active BCIs.…”