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:

Journal: Sleep

Article Title: Activity of a subset of vesicular GABA-transporter neurons in the ventral zona incerta anticipates sleep onset

doi: 10.1093/sleep/zsaa268

Figure Lengend Snippet: Classification of transition points between wake–sleep states. The wake–sleep states were partitioned based on EEG, EMG, EEG power spectra and video recording of the animal behavior, criteria that were same as in our previous study [19]. The transition point is indicated by the gray dashed vertical line.

Article Snippet: Data analysis of calcium traces during sleep–wake states The EEG and EMG signals that were recorded via OmniPlex® (Plexon.com) were imported into a data visualization and physiological analysis software program (Neuroexplorer.com).

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: 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: Nature Communications

Article Title: Astrocytic chloride is brain state dependent and modulates inhibitory neurotransmission in mice

doi: 10.1038/s41467-023-37433-9

Figure Lengend Snippet: a [Cl − ] i in cortical astrocytes was imaged using mClY and fibre photometry in combination with EEG/EMG recordings in awake, freely moving, or spontaneously sleeping mice. b Representative traces of astrocytic [Cl − ] i , EEG, and EMG; colour code indicates sleep and awake periods. c Changes in [Cl − ] i during transiting from sleep to awake or from awake to sleep in expanded time scale. d Distribution of astrocytic [Cl − ] i during sleep and wakefulness. N = 1 representative mouse. e Average [Cl − ] i traces during transition from sleep to awake or awake to sleep, shading indicates ±SEM (standard error of the mean). N = 6 mice. f Mean [Cl − ] i and standard deviation (SD) during sleep and wakefulness. N = 6 mice, paired two-tailed t -test * p = 0.0296, ** p = 0.002. g Distribution of [Cl − ] i in awake and sleep states recorded from freely moving and naturally sleeping mice. N = 6 mice, paired two-tailed t -test, **** p < 0.001. h Distribution of YFP recorded from freely moving and naturally sleeping mice. N = 3 mice. i [Cl − ] i in cortical astrocytes was imaged in awake and resting (immobile) or voluntary running (mobile, 10 s immobility followed by more than 1 s mobility) mice. j Cross correlation of [Cl − ] i versus SD of EMG. Data represent mean ± SEM. N = 6 mice, the average Pearson correlation coefficient: 0.258. k Representative traces of astrocytic [Cl − ] i , EEG, and EMG; colour code indicates mobile and immobile periods. l [Cl − ] i trace during transition from immobile to mobile state, shading indicates ±SD. N = 7 mice. m Mean [Cl − ] i ( N = 7 mice) and standard deviation ( N = 6 mice) during immobile and mobile periods. Paired two-tailed t -test, ** p = 0.0096, p = 0.7490. n Distribution of [Cl − ] i recorded from awake freely moving, mobile or immobile mice. N = 6 mice, one sample t -test, **** p < 0.001. o Relative changes of [Cl − ] i when transitioning between sleep and awake ( N = 6 mice) versus immobile and mobile ( N = 7 mice). Paired two-tailed t -test. [Cl − ] i = mClY − ΔF/F (%). Data represent mean ± SEM. Source data are provided as a Source Data file.

Article Snippet: On the day of recording, mice were connected to the fibre optic implants and recordings were performed for 2–4 h. EEG and EMG signals were amplified (National Instruments Inc.) and filtered (EEG signal: high-pass at 1 Hz and low-pass at 100 Hz; EMG signal: high-pass at 10 Hz and low-pass at 100 Hz), and a notch filter of 50 Hz was used to reduce power line noise.

Techniques: Standard Deviation, Two Tailed Test