Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

Tsuchimoto, Shohei; Shibusawa, Shuka; Iwama, Seitaro; Hayashi, Masaaki; Okuyama, Kohei; Mizuguchi, Nobuaki; Kato, Kenji; Ushiba, Junichi

doi:10.1016/j.jneumeth.2021.109089

Cited by 34 publications

(26 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…C3 and C4, respectively). This method enabled us to extract the task-related EEG signature and improve the signal-to-noise ratio of SMR signals ( McFarland et al, 1997 ; Tsuchimoto et al, 2021 ). In addition, the large Laplacian method is better matched to the topographical extent of the EEG control signal than the small Laplacian and ear reference methods McFarland et al, 1997 ; (3) a fast Fourier transform was applied to the spatially large Laplacian filtered EEG signals; (4) the power spectrum was calculated by calculating the square of the Fourier spectrum; (5) the alpha band power was obtained by averaging the power spectrum across the predefined alpha target frequencies from the EEG calibration session (described below); (6) the alpha band power was time-smoothed by averaging across the last five windows (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…The EEG signal underwent a 1–70 Hz, second-order Butterworth bandpass filter and a 50 Hz notch filter. The EEG signals of all channels were spatially filtered using a common average reference, which subtracted the average value of the entire electrode montage (the common average) from that of the channel of interest to remove global noise ( McFarland et al, 1997 ; Tsuchimoto et al, 2021 ). EEG channels in each trial were rejected during further analysis if they contained an amplitude above 100 μV ( Sanei and Chambers, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Hayashi

Okuyama

Mizuguchi

et al. 2022

eLife

Self Cite

View full text Add to dashboard Cite

Human behavior requires interregional crosstalk to employ the sensorimotor processes in the brain. Although external neuromodulation techniques have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We addressed this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable the participants to modulate their bilateral sensorimotor activities. Herein, we report that participants learn to up- and down-regulate the ipsilateral excitability to the imagined hand while maintaining constant the contralateral excitability; this modulates the magnitude of interhemispheric inhibition (IHI) assessed by the paired-pulse transcranial magnetic stimulation (TMS) paradigm. Further physiological analyses revealed that the manipulation capability of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results show an interesting approach for neuromodulation, which might identify new treatment opportunities, for example, in patients suffering from a stroke.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Hayashi

Okuyama

Mizuguchi

et al. 2022

eLife

Self Cite

View full text Add to dashboard Cite

show abstract

“…EEG signals around the left SM1 (i.e., channel C3) were only used to detect the attempted movement, because accumulated evidence suggests that event-related desynchronization of SMR (SMR-ERD) contralateral to the hand that attempted to move reflects the excitability of SM1 (Hummel et al, 2002; Naros et al, 2019; Takemi et al, 2013). In online processing, a large Laplacian filter was applied to EEG signals from channel C3 to extract sensorimotor activity (McFarland et al, 1997; Tsuchimoto et al, 2021). Subsequently, the band power of SMR (SMR-power; 8-13 Hz) was extracted by Fourier transform with a 1-s window and Hamming window function.…”

Section: Methodsmentioning

confidence: 99%

De novo brain-computer interfacing deforms manifold of populational neural activity patterns in human cerebral cortex

Iwama

Zhang

Ushiba

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Human brains are capable of modulating innate activities to adapt to novel environmental stimuli; for sensorimotor cortices (SM1) this means acquisition of a rich repertoire of motor behaviors. We investigated the adaptability of human SM1 motor control by analyzing net neural population activity during the learning of brain-computer interface (BCI) operations. We found systematic interactions between the neural manifold of cortical population activities and BCI classifiers; the neural manifold was stretched by rescaling motor-related features of electroencephalograms with model-based fixed classifiers, but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, operation of a BCI based on a de novo classifier with a fixed decision boundary based on biologically unnatural features, deformed the neural manifold to be orthogonal to the boundary. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.

show abstract

“…The real-time SMR-ERD intensity in each hemisphere (relative to the average power of the 1-5 s of the resting epoch) was obtained every 100 ms and calculated using the last 1-s data as follows (Hayashi et al, 2020): (1) acquired raw EEG signals recorded over SM1 underwent a 1–70-Hz second-order Butterworth bandpass filter and a 50-Hz notch filter; (2) filtered EEG signals were spatially filtered with a large Laplacian (60 mm to set of surrounding channels), which subtracted the average value of the surrounding six channel montage from that of the channel of interest (i.e., C3 and C4, respectively). This method enabled us to extract the task-related EEG signature and improve the signal-to-noise ratio of SMR signals (McFarland et al, 1997; Tsuchimoto et al, 2021). In addition, the large Laplacian method is better matched to the topographical extent of the EEG control signal than the small Laplacian and ear reference methods (McFarland et al, 1997); (3) a fast Fourier transform was applied to the spatially large Laplacian filtered EEG signals; (4) the power spectrum was calculated by calculating the square of the Fourier spectrum; (5) the alpha band power was obtained by averaging the power spectrum across the predefined alpha target frequencies from the EEG calibration session (described below); (6) the alpha band power was time-smoothed by averaging across the last five windows (i.e., 500 ms) to extract the low-frequency component for high controllability.…”

Section: Methodsmentioning

confidence: 99%

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric rebalancing of M1 excitability

Hayashi

Okuyama

Mizuguchi

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Human behavior requires interregional crosstalk to employ the sensorimotor processes in the brain. Although some external neuromodulation tools have been used to manipulate interhemispheric sensorimotor activity, a central controversy concerns whether this activity can be volitionally controlled. Experimental tools lack the power to up- or down-regulate the state of the targeted hemisphere over a large dynamic range and, therefore, cannot evaluate the possible volitional control of the activity. We overcame this difficulty by using the recently developed method of spatially bivariate electroencephalography (EEG)-neurofeedback to systematically enable participants to manipulate their bilateral sensorimotor activities. Herein, we report that bi-directional changes in ipsilateral excitability to the imagined hand (conditioning hemisphere) affect interhemispheric inhibition (IHI) assessed by paired-pulse transcranial magnetic stimulation paradigm. In addition, participants were able to robustly manipulate the IHI magnitudes. Further physiological analyses revealed that the self-manipulation of IHI magnitude reflected interhemispheric connectivity in EEG and TMS, which was accompanied by intrinsic bilateral cortical oscillatory activities. Our results provide clear neuroscientific evidence regarding the inhibitory interhemispheric sensorimotor activity and IHI manipulator, thereby challenging the current theoretical concept of recovery of motor function for neurorehabilitation.

show abstract

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity

Cited by 34 publications

References 51 publications

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric inhibition

De novo brain-computer interfacing deforms manifold of populational neural activity patterns in human cerebral cortex

Spatially bivariate EEG-neurofeedback can manipulate interhemispheric rebalancing of M1 excitability

Contact Info

Product

Resources

About