Freezing of gait detection in Parkinson's disease via multimodal analysis of EEG and accelerometer signals.

Parkinson's disease (PD) patients with freezing of gait (FOG) can suddenly lose their forward moving ability leading to unexpected falls. To overcome FOG and avoid the falls, a real-time accurate FOG detection or prediction system is desirable to trigger on-demand cues. In this study, we designed and implemented an in-place movement experiment for PD patients to provoke FOG and meanwhile acquired multimodal physiological signals, such as electroencephalography (EEG) and accelerometer signals. A multimodal model using brain activity from EEG and motion data from accelerometers was developed to improve FOG detection performance. In the detection of over 700 FOG episodes observed in the experiments, the multimodal model achieved 0.211 measured by Matthews Correlation Coefficient (MCC) compared with the single-modal models (0.127 or 0.139).Clinical Relevance- This is the first study to use multimodal: EEG and accelerometer signal analysis in FOG detection, and an improvement was achieved.

Preparing to Stop Action Increases Beta Band Power in Contralateral Sensorimotor Cortex.

How do we prepare to stop ourselves in the future? Here, we used scalp EEG to test the hypothesis that people prepare to stop by putting parts of their motor system (specifically, here, sensorimotor cortex) into a suppressed state ahead of time. On each trial, participants were cued to prepare to stop one hand and then initiated a bimanual movement. On a minority of trials, participants were instructed to stop the cued hand while continuing quickly with the other. We used a guided multivariate source separation method to examine oscillatory power changes in presumed sensorimotor cortical areas. We observed that, when people prepare to stop a hand, there were above-baseline beta band power increases (12-24 Hz) in contralateral cortex up to a second earlier. This increase in beta band power in the proactive period was functionally relevant because it predicted, trial by trial, the degree of selectivity with which participants subsequently stopped a response but did not relate to movement per se. Thus, preparing to stop particular response channels corresponds to increased beta power from contralateral (sensorimotor) cortex, and this relates specifically to subsequent stopping. These results provide a high temporal resolution and frequency-specific electrophysiological signature of the preparing-to-stop state that is pertinent to future studies of mitigating provocation, including in clinical disorders. The results also highlight the utility of guided multivariate source separation for revealing the cortical dynamics underlying both movement and response suppression.