Is implicit motor imagery a reliable strategy for a brain computer interface?

Explicit motor imagery (eMI) is a widely used brain computer interface (BCI) paradigm, but not everybody can accomplish this task. Here we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgement task is known to require mental rotation of a person’s own hands which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography (EEG) was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left and right hand the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left and 78 ± 7% for the right hand which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special brain computer interface based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement or for treatment of bodily spatial neglect

Effect of mindfulness meditation on brain–computer interface performance

Electroencephalogram based Brain-Computer Interfaces (BCIs) enable stroke and motor neuron disease patients to communicate and control devices. Mindfulness meditation has been claimed to enhance metacognitive regulation. The current study explores whether mindfulness meditation training can thus improve the performance of BCI users. To eliminate the possibility of expectation of improvement influencing the results, we introduced a music training condition. A norming study found that both meditation and music interventions elicited clear expectations for improvement on the BCI task, with the strength of expectation being closely matched. In the main 12 week intervention study, seventy-six healthy volunteers were randomly assigned to three groups: a meditation training group; a music training group; and a no treatment control group. The mindfulness meditation training group obtained a significantly higher BCI accuracy compared to both the music training and notreatment control groups after the intervention, indicating effects of meditation above and beyond expectancy effects

Development and characterization of an intracortical closed-loop brain-computer interface

Intracortical brain-computer interfaces (BCI) have the potential to restore motor function to people with paralysis by extracting movement intent signals directly from motor cortex. While current technology has allowed individuals to perform simple object interactions with robotic arms, such demonstrations have depended exclusively on visual feedback. Additional forms of sensory feedback may lessen the dependence on vision and allow for more dexterous control. Intracortical microstimulation (ICMS) has been proposed as a method of adding somatosensory feedback to BCI by directly stimulating somatosensory cortex to evoke tactile sensations referred to the hand. Our lab recently demonstrated that ICMS can elicit graded and focal tactile sensations in an individual with spinal cord injury (SCI). However, several challenges must be resolved to demonstrate the viability of ICMS as a technique for incorporating sensory feedback in a closed-loop BCI. First, microstimulation generates large voltage transients that appear as artifacts in the neural recordings used for BCI control. These artifacts can corrupt the recorded signal throughout the entire stimulus train, and must be eliminated to allow for continuous BCI decoding. Second, it is unknown whether the sensations elicited by ICMS can be perceived quickly enough for use as a feedback signal. Here, I present several aspects of the development of a closed-loop BCI system, including a method for artifact rejection and the characterization of simple reaction times to ICMS of human somatosensory cortex. A human participant with tetraplegia due to SCI was implanted with four microelectrode arrays in primary motor and somatosensory cortices. I implemented a robust method of artifact rejection that preserves neural data as soon as 750 microseconds after each stimulus pulse by applying signal blanking and an appropriate digital filter. I validated this method by comparing BCI performance with and without ICMS and found that performance was maintained with ICMS and artifact rejection. Next, I characterized simple reaction times to single-channel ICMS, and found that responses to ICMS were comparable, and often faster, than responses to electrical stimulation on the hand. These findings suggest that ICMS is a viable method to provide feedback in a closed-loop BCI

Framework for Electroencephalography-based Evaluation of User Experience

Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to estimate continuously the user's mental workload, attention and recognition of interaction errors during different interaction tasks. We validate these measures on a controlled virtual environment and show how they can be used to compare different interaction techniques or devices, by comparing here a keyboard and a touch-based interface. Thanks to such a framework, EEG becomes a promising method to improve the overall usability of complex computer systems.Comment: in ACM. CHI '16 - SIGCHI Conference on Human Factors in Computing System, May 2016, San Jose, United State