Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: A summary of select literature examples using CNN models with EEG or EMG signals.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: The protocol followed to process the EEG/EMG signals, generate the spectrogram and signal images, and train the CNN models using different EEG–EMG fusion methods. The top path (purple) shows the steps used to develop the CNN models based on spectrogram image inputs, while the bottom path (green) shows the steps used to develop the CNN models based on signal image inputs. For all EEG–EMG-fusion-based CNN model types (represented by the final step of all paths), an EEG and EMG only version was also trained, to provide a baseline comparison for evaluating EEG–EMG Fusion.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: Comparison

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: A sample normalized spectrogram image to demonstrate the three EEG–EMG fusion methods used, where (A,B) show single-channel spectrograms and (C) visualizes a multi-channel spectrogram. (A) Shows the grouped method, where signal channels of the same type are grouped together within the image. (B) Shows the mixed method, where EEG and EMG channels are alternated to mix signal types. (C) Provides a visualization of the stacked method, where a multi-channel spectrogram is generated by combining the different EEG/EMG spectrograms in depth-wise manner.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: Generated

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the single-channel spectrogram-based CNN models. (A) Shows the matrix for the grouped fusion method while (B) shows the matrix for the mixed fusion method. (C,D) Show the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the 1D convolution signal-image-based CNN models. (A) Shows the matrix for the EEG–EMG fusion model, while (B,C) how the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the split convolution signal-image-based CNN models. (A) Shows the matrix for the EEG–EMG fusion model, while (B,C) how the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Evaluating Muscle Activation Models for Elbow Motion Estimation

doi: 10.3390/s18041004

Figure Lengend Snippet: The data collection setup consists of the Intronix 2024F Isolated Amplifier and the elbow position collection brace. Bipolar surface EMG electrodes used by the amplifier to collect the electrical muscle activity. One pair of electrodes was placed over the skin covering each of the biceps brachii and triceps brachii muscles, while a single reference electrode was placed near the olecranon. Subjects were connected to the collection brace using Velcro straps (not shown) such that their elbow axis aligned with the rotational axis of the brace. An Arduino Uno was used to collect encoder data from the collection brace and stream it to a desktop computer.

Article Snippet: The skin over the muscle of interest was cleaned using alcohol, and a surface EMG electrode was placed on the skin and connected to an Intronix 2024F Isolated Amplifier (Intronix Technologies Corporation, Bolton, ON, Canada), which sampled the EMG signals at a frequency of 4000 Hz.

Techniques: Isolation, Activity Assay, Muscles