Locomotion, Chris Sawyer's Free Download » GOG Unlocked

we tried to keep the same device setup between each arm. however, we couldnt keep the same device type because the original data is a research project and its protocol design was not adequate for commercial purposes.

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the figures below show the real and imaginary parts of the electrophysiological data (averaged across trials) for each of the electrodes. the four channels were combined into virtual scalp eeg electrodes that represent the neural activity in the brains surface in preparation for source reconstruction (fig. 4).

the phase-synchronized data from the different body sensors and the global averages were combined to obtain a single movement event as depicted in figure 5. to extract features related to the gait task, four main body sensors were used as follows: hip, knee, ankle, and thorax. the time and frequency domain data were used to extract features in both the time and frequency domains. the features were obtained for both the foot and leg, and as such, the features are commonly referred to as features extracted from the foot and features extracted from the leg.

for each site, the features were extracted at the electrode level, then averaged across subjects. the averaged data from the four sites were combined into virtual scalp eeg electrodes, as shown in figure 6. the next step was to combine the features extracted from the body sensors with those extracted from the scalp signals. the scalp features were obtained in the time domain (epochs) and frequency domain (bands). this allowed us to extract features (e.g., amplitude and frequency) that were related to the movement execution. the spatiotemporal domain included three features: time-to-peak, duration, and the change in duration.

https://www.columbusbikeracing.org/group/news-events/discussion/6963fe49-147f-43e6-a8e1-e28279d96c68