11 research outputs found
MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware
Neurophysiological studies are typically conducted in laboratories with
limited ecological validity, scalability, and generalizability of findings.
This is a significant challenge for the development of brain-computer
interfaces (BCIs), which ultimately need to function in unsupervised settings
on consumer-grade hardware. We introduce MYND: A framework that couples
consumer-grade recording hardware with an easy-to-use application for the
unsupervised evaluation of BCI control strategies. Subjects are guided through
experiment selection, hardware fitting, recording, and data upload in order to
self-administer multi-day studies that include neurophysiological recordings
and questionnaires. As a use case, we evaluate two BCI control strategies
("Positive memories" and "Music imagery") in a realistic scenario by combining
MYND with a four-channel electroencephalogram (EEG). Thirty subjects recorded
70.4 hours of EEG data with the system at home. The median headset fitting time
was 25.9 seconds, and a median signal quality of 90.2% was retained during
recordings.Neural activity in both control strategies could be decoded with an
average offline accuracy of 68.5% and 64.0% across all days. The repeated
unsupervised execution of the same strategy affected performance, which could
be tackled by implementing feedback to let subjects switch between strategies
or devise new strategies with the platform.Comment: 9 pages, 5 figures. Submitted to PNAS. Minor revisio
Distance- and speed-informed kinematics decoding improves M/EEG based upper-limb movement decoder accuracy
Objective. One of the main goals in brain–computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy. Approach. In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics. Main results. At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories. Significance. We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF