Somatosensory evoked potentials (SSEPs) have been established as an electrophysiological tool for the prognostication of neurological outcome in patients with hypoxic-ischemic brain injury. The early and late responses in SSEPs reflect the sequential activation of neural structures along the somatosensory pathway. This study reports that the SSEP can be separated into early (shortlatency, SL) and late (long-latency, LL) responses using Independent Component Analysis (ICA), based on the assumption that these components are generated from different neural sources. Moreover, this source separation into the SL and LL components allows analysis of electrophysiological response to brain injury, even when the SSEPs are severely distorted and SL and LL components get mixed. With the help of ICA decomposition and corrected peak estimation, the latency of LL-SSEP is shown to be predictive of long-term neurological outcome. Further, it is shown that the recovery processes of SL-and LL-SSEPs follow different dynamics, with the SL-SSEP restored earlier than LL-SSEP. We predict that the SL-and LL-SSEPs reflect the timing of the progression of evoked response through the thalamocortical pathway and as such respond differently depending upon injury and recovery of the thalamic and cortical regions, respectively.
The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r 2 ) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.
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