Journal: PLOS ONE

Article Title: Feature extraction method of EEG based on wavelet packet reconstruction and deep learning model of VR motion sickness feature classification and prediction

doi: 10.1371/journal.pone.0305733

Figure Lengend Snippet: (a) Data Collection and Processing: Primarily, meticulous acquisition and processing of EEG signals and questionnaire data lay the foundation for this method. Raw data undergoes a transformational journey to become structured feature vectors conducive to computational analysis. Preprocessing involves the application of the wavelet packet transform to EEG data, stratifying it into δ, θ, α, and β frequency components. This nuanced stratification aids in comprehending evolving EEG features over time, enhancing the model’s predictive accuracy. (b) Model Training: The training phase involves feeding preprocessed EEG signals and questionnaire data into the model’s architecture. A Recurrent Neural Network (RNN) takes charge, extracting distinct features characterizing different states. These features, essential for subsequent modeling, are then mapped to questionnaire responses, enabling the model to discern motion sickness states. (c) Test Data Evaluation: The final step involves rigorous evaluation using a classifier and predictor to assess the model’s performance on test data. This phase offers insights into the model’s generalization ability and predictive accuracy.

Article Snippet: Cognionics AIM is used to collect EEG signals at a frequency of 500hz.

Techniques:

Journal: Neuroimage: Reports

Article Title: Null effects of musical groove on cortico-muscular coherence during isometric contraction

doi: 10.1016/j.ynirp.2021.100075

Figure Lengend Snippet: EEG-EMG coherence spectra averaged across conditions for the hand and foot. Red lines represent the average of the three Groove conditions with shading representing 95% confidence intervals (CI) and grey lines representing individual participants. Grey shaded areas represent the selected beta range (15–35 Hz) and the topographical maps show the distribution of coherence values averaged within this range across participants. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Article Snippet: EEG and EMG signals were recorded at a sampling rate of 2048 Hz using a BioSemi Active-Two system (BioSemi, Amsterdam, Netherlands).

Techniques:

Journal: Neuroimage: Reports

Article Title: Null effects of musical groove on cortico-muscular coherence during isometric contraction

doi: 10.1016/j.ynirp.2021.100075

Figure Lengend Snippet: Mean beta (15–35 Hz) CMC (A), Mean beta (15–35 Hz) EEG power (B), Mean broadband EMG (C), and Mean beta (15–35 Hz) EMG power (D) as a function of the limb and experimental conditions. Error bars represent 95% confidence intervals (CI). Individual data points are shaded.

Article Snippet: EEG and EMG signals were recorded at a sampling rate of 2048 Hz using a BioSemi Active-Two system (BioSemi, Amsterdam, Netherlands).

Techniques: