An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was approximately 25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.

An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N=81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N=38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.

Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.