A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Feature name</td><td class="align_center">Description</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">EMG_Peaks</td><td class="align_center">This gives out the number of local maxima in a minute</td></tr><tr><td class="align_left">EMG_Average_Amplitude</td><td class="align_center">This feature gives out the average amplitude of the local maximas in a minute</td></tr><tr><td class="align_left">EMG_Differ_Mean</td><td class="align_center">This feature pursues the average difference between consecutive local maxima in a minute</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Electromyography (EMG) features.</div>

Journal of Healthcare Engineering

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Table 3

Table 3: A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices 

Table 3 | A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices 