High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing

Iwama, Seitaro; Morishige, Masumi; Kodama, Midori; Takahashi, Yoshikazu; Hirose, Ryotaro; Ushiba, Junichi

doi:10.1038/s41597-023-02260-6

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Four scalp EEG data collections (N=138; N = 30, 30, 22 and 56, respectively) during resting and motor imagery tasks, measured by our research group, were analyzed in this study [37]. All experiments were performed based on the Declaration of Helsinki and their protocols were by the Ethics Committee of the Faculty of Science and Technology, Keio University (IRB approval number: 2020-38 or 2021-74).…”

Section: B Datasetmentioning

confidence: 99%

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation

Iwama,

Ushiba

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Rapid and robust identification of the individual alpha frequency (IAF) in electroencephalogram (EEG) is an essential factor for successful brain-computer interface (BCI) use. Here we demonstrate an algorithm to determine the IAF from short-term resting-state scalp EEG data. Methods: First, we outlined the algorithm to determine IAF from short-term resting scalp EEG data and evaluated its reliability using a large-scale dataset of scalp EEG during motor imagery-based BCI use and independent dataset for generalizability confirmation (N = 147). Next, we characterized the relationship between IAF and responsive frequency band of sensorimotor rhythm, which exhibits prominent eventrelated desynchronization (SMR-ERD) while attempting unilateral and movement. Results: The proposed sequential Bayesian estimation algorithm (Rapid-IAF) determined IAF from less than 26-second resting EEG data among 95% of participants, indicating a clear advance over the conventional methods, which uses 2-15 minutes of data in previous literatures. We confirmed that the determined IAF corresponded to the frequency of SMR, which exhibits the most prominent event-related desynchronization during BCI use (individual SMR-ERD frequency, ISF). Moreover, intraclass correlation revealed that the estimated IAF was more stable than ISF across sessions, suggesting its reliability and utility for robust BCI use without intermittent recalibration. Conclusion In summary, our method rapidly and reliably determined IAF compared to the conventional method using the spectral power change based on task-related response. The method can be utilized to quick BCI initialization. Significance: The demonstration of rapid, task-free parametrization of individual variability of neural responses would be of importance for future BCI systems including neural communication via a cursor, an avatar or robots, and closedloop neurofeedback training.

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Section: B Datasetmentioning

confidence: 99%

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation

Iwama,

Ushiba

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke

Sun,

Dai,

et al. 2024

Medical Review

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Persistent motor deficits are highly prevalent among post-stroke survivors, contributing significantly to disability. Despite the prevalence of these deficits, the precise mechanisms underlying motor recovery after stroke remain largely elusive. The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research. Quantitative electroencephalography (qEEG) parameters, including the power ratio index, brain symmetry index, and phase synchrony index, have emerged as potential prognostic markers for overall motor recovery post-stroke. Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery. However, accurately identifying the source activity poses a challenge, prompting the integration of EEG with other neuroimaging modalities, such as functional near-infrared spectroscopy (fNIRS). fNIRS is nowadays widely employed to investigate brain function, revealing disruptions in the functional motor network induced by stroke. Combining these two methods, referred to as integrated fNIRS-EEG, neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features, which may be more accurate than the individual modality alone. By harnessing integrated fNIRS-EEG source localization, brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke, providing valuable insights into the assessment and treatment of post-stroke motor recovery.

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High-density scalp electroencephalogram dataset during sensorimotor rhythm-based brain-computer interfacing

Cited by 2 publications

References 55 publications

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation

Rapid-IAF: Rapid Identification of Individual Alpha Frequency in EEG Data Using Sequential Bayesian Estimation

Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke

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