Localizing Current Dipoles from EEG Data Using a Birth–Death Process

Nakamura, Keita; Sonoda, Shozo; Hino, Hideitsu; Kawasaki, Masahiro; Akaho, Shotaro; Motomura, Noboru

doi:10.1109/bibm.2018.8621504

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…We employed this method to estimate the IAF, stable during a 10-minute BCI session, from the variable samples of IAF identified from short-term EEG signals. The algorithm has been employed in the other fields of biosignal processing including scalp EEG to estimate the internal parameters from the limited acquired data [24], [25]. Using a large-scale…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%