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  • 1
    Online Resource
    Online Resource
    Neural decoding of electrocorticographic signals using dynamic mode decomposition
    IOP Publishing ; 2020
    In:  Journal of Neural Engineering Vol. 17, No. 3 ( 2020-06-01), p. 036009-
    Details
    In: Journal of Neural Engineering, IOP Publishing, Vol. 17, No. 3 ( 2020-06-01), p. 036009-
    Abstract: Objective. Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotemporal patterns characterizing movement types to increase the amount of information gained from these signals. Approach . Previous studies have demonstrated that motor information could be decoded using powers of specific frequency bands of the ECoG signals estimated by fast Fourier transform (FFT) or wavelet analysis. However, because FFT is evaluated for each channel, the temporal and spatial patterns among channels are difficult to evaluate. Here, we used dynamic mode decomposition (DMD) to evaluate the spatiotemporal pattern of ECoG signals and evaluated the accuracy of motor decoding with the DMD modes. We used ECoG signals during three types of hand movements, which were recorded from 11 patients implanted with subdural electrodes. From the signals at the time of the movements, the modes and powers were evaluated by DMD and FFT and were decoded using support vector machine. We used the Grassmann kernel to evaluate the distance between modes estimated by DMD (DMD mode). In addition, we decoded the DMD modes, in which the phase components were shuffled, to compare the classification accuracy. Main results. The decoding accuracy using DMD modes was significantly better than that using FFT powers. The accuracy significantly decreased when the phases of the DMD mode were shuffled. Among the frequency bands, the DMD mode at approximately 100 Hz demonstrated the highest classification accuracy. Significance. DMD successfully captured the spatiotemporal patterns characterizing the movement types and contributed to improving the decoding accuracy. This method can be applied to improve BCIs to help severely paralyzed patients communicate.
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    URL: Article
    DOI: 10.1088/1741-2552/ab8910
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2135187-9
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Magnetoencephalographic neurofeedback training decreases β-low-γ phase-amplitude coupling of the motor cortex of healthy adults: a double-blinded randomized crossover feasibility study
    IOP Publishing ; 2023
    In:  Journal of Neural Engineering Vol. 20, No. 3 ( 2023-06-01), p. 036005-
    Details
    In: Journal of Neural Engineering, IOP Publishing, Vol. 20, No. 3 ( 2023-06-01), p. 036005-
    Abstract: Objective. The coupling between the beta (13–30 Hz) phase and low gamma (50–100 Hz) amplitude in the motor cortex is thought to regulate motor performance. Abnormal phase-amplitude coupling (PAC) of beta-low gamma ( β -low- γ PAC) is associated with motor symptoms of Parkinson’s disease. However, the causal relationship between β -low- γ PAC and motor performance in healthy subjects is unknown. We hypothesized that healthy subjects could change the strength of the β -low- γ PAC in the resting state by neurofeedback training (NFT) to control the β -low- γ PAC, such that the motor performance changes in accordance with the changes in β -low- γ PAC in the resting state. Approach. We developed an NFT to control the strength of the β -low- γ PAC in the motor cortex, which was evaluated by magnetoencephalography (MEG) using a current source estimation technique. Twenty subjects were enrolled in a double-blind randomized crossover trial to test the feasibility of the MEG NFT. In the NFT for 2 d, the subjects were instructed to reduce the size of a black circle whose radius was proportional (down-training) or inversely proportional (up-training) to the strength of the β -low- γ PAC. The reaction times (RTs) to press a button according to some cues were evaluated before and after training. This study was registered at ClinicalTrials.gov (NCT03837548) and UMIN-CTR (UMIN000032937). Main results. The β -low- γ PAC during the resting state was significantly decreased after down-training, although not significantly after up-training. RTs tended to decrease after both trainings, however the differences were not statistically significant. There was no significant correlation between the changes in β -low- γ PAC during rest and RTs. Significance. The proposed MEG NFT was demonstrated to change the β -low- γ PAC of the motor cortex in healthy subjects. However, a relationship between PAC and RT has not yet been demonstrated.
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    URL: Article
    DOI: 10.1088/1741-2552/acd0d6
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2023
    detail.hit.zdb_id: 2135187-9
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Abnormal phase–amplitude coupling characterizes the interictal state in epilepsy
    IOP Publishing ; 2022
    In:  Journal of Neural Engineering Vol. 19, No. 2 ( 2022-04-01), p. 026056-
    Details
    In: Journal of Neural Engineering, IOP Publishing, Vol. 19, No. 2 ( 2022-04-01), p. 026056-
    Abstract: Objective. Diagnosing epilepsy still requires visual interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from EEG and MEG, such as relative power (Power) and functional connectivity (FC). However, the usefulness of interictal phase–amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. Approach. We obtained resting-state MEG and magnetic resonance imaging (MRI) in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and MRI to calculate Power in the δ (1–3 Hz), θ (4–7 Hz), α (8–13 Hz), β (13–30 Hz), low γ (35–55 Hz), and high γ (65–90 Hz) bands and FC in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α , and β and the amplitudes of low and high γ . First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, FC, and features extracted by deep learning (DL) individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. Main results. The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ /low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and DL. Significance. Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    URL: Article
    DOI: 10.1088/1741-2552/ac64c4
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2135187-9
    SSG: 12
    Location Call Number Limitation Availability
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  • 4
    Online Resource
    Online Resource
    Data-driven electrophysiological feature based on deep learning to detect epileptic seizures
    IOP Publishing ; 2021
    In:  Journal of Neural Engineering Vol. 18, No. 5 ( 2021-10-01), p. 056040-
    Details
    In: Journal of Neural Engineering, IOP Publishing, Vol. 18, No. 5 ( 2021-10-01), p. 056040-
    Type of Medium: Online Resource
    ISSN: 1741-2560 , 1741-2552
    URL: Article
    DOI: 10.1088/1741-2552/ac23bf
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2021
    detail.hit.zdb_id: 2135187-9
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
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