Decoding finger movement in humans using synergy of EEG cortical current signals

Yoshimura, Natsue; Tsuda, Hayato; Kawase, Toshihiro; Kambara, Hiroyuki; Koike, Yasuharu

doi:10.1038/s41598-017-09770-5

Cited by 30 publications

(24 citation statements)

References 58 publications

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“…The VBMEG method was initially proposed to investigate the dynamic cortical activity of healthy participants during a face recognition task (Fukushima et al, 2015 ). VBMEG has been tested in both simulations (Sato et al, 2004 ; Aihara et al, 2012 ) and healthy volunteer studies (Yoshioka et al, 2008 ; Yoshimura et al, 2012 , 2017 ; Nakamura et al, 2015 ); their main focus was on muscle activity reconstruction and visual stimuli analysis with or without structural and functional MRI constraints. Nevertheless, as a novel brain imaging method, the clinical value of the VBMEG method is yet to be demonstrated regarding its potential to investigate functional brain changes following a brain disease such as a stroke.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Filatova

Yang

Dewald

et al. 2018

Front. Neural Circuits

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In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.

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Section: Introductionmentioning

confidence: 99%

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Filatova

Yang

Dewald

et al. 2018

Front. Neural Circuits

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“…The mu-wave is EEG signal with frequency range lies in 8 to 13 Hz. The previous researcher has reported that [6,7,8] the decrease in mu-wave activity over the motor cortex of the brain shows the presence of information processing related to planning, decisionmaking, and preparation of motoric activities.…”

Section: A Emg and Eeg Signal Characteristic On Motoricmentioning

confidence: 98%

“…Biofeedback quantification using electroencephalography (EEG) is also an intensive research [6,7,8] related to motor activities. A mu-wave has reported as one of the parameters for quantifying the level of desynchronization of neuron on the motor cortex [8,9,24].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

2020

International Journal of Biology and Biomedical Engineering

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Effective segmentation of electromyography (EMG) burst that synchronizes with electroencephalography (EEG) for long-duration recording is important steps to better understand the quantification of brain-muscle connectivity in periodic motoric activities. The work proposes an alternative automatic EMG segmentation scheme consists of four main steps, i.e. denoising of EMG burst signal using discrete wavelet transform, enveloping signal using time-windows averaging of RMS amplitude, an adaptive threshold to detect start/end burst envelope with accommodation of muscle contraction characteristic and the final step is conversion enveloping signal to binary segmentation signal.The proposed scheme is evaluated to detect contraction period/duration of EMG for the subject under repetitive holding and releasing grasp using a physiotherapy device. During exercise, the bio-amplifier board is customized to acquire simultaneous EEG and EMG from the region of flexor digitorum superficialis (FDS) of muscle and cortical motor of the brain, with total 284 EMG burst that counting by manual segmentation. The automatic segmentation can detect the total EMG burst by 6.25% error of false burst detection.The usefulness of proposed scheme is also tested to association analysis according to the power of EMG burst and the power of mu-wave of EEG recorded on the motor cortex. The changing trend of the power of mu-wave associated with muscle relaxation, muscle contraction strength and the synchronization level on the motor cortex during exercise are analyzed with integrated information that is relevant with biofeedback concept. The results demonstrate that proposed scheme has potential to be an effective method for the evaluation of biofeedback rehabilitation exercise.

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“…In the realm of brain-machine interfaces (BMIs), attempts have been made to decode brain activity to allow the input of commands into BMI systems. Due to its practical advantages, electroencephalography (EEG) has been widely used in BMI systems to infer information about intention to move the upper limb [1], targets and distractors [2], finger movement [3], resting states or motor attempts to move the paretic hand [4], intention to stand or sit [5], and intended direction of movement [6,7]. Information that is not directly used could still be useful for improving BMI systems.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Delayed Response during Real-Time Cursor Control Using Electroencephalography

Kim

Yoshimura

Koike

2020

Journal of Healthcare Engineering

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Error-related brain activation has been investigated for advanced brain-machine interfaces (BMI). However, how a delayed response of cursor control in BMI systems should be handled is not clear. Therefore, the purpose of this study was to investigate how participants responded to delayed cursor control. Six subjects participated in the experiment and performed a wrist-bending task. For three distinct delay intervals (an interval where participants could not perceive the delay, an interval where participants could not be sure whether there was a delay or not, and an interval where participants could perceive the delay), we assessed two types of binary classifications (“Yes + No” vs. “I don’t know” and “Yes” vs. “No”) based on participants’ responses and applied delay times (thus, four types of classification, overall). For most participants, the “Yes vs. No” classification had higher accuracy than “Yes + No” vs. “I don’t know” classification. For the “Yes + No” vs. “I don’t know” classification, most participants displayed higher accuracy based on response classification than delay classification. Our results demonstrate that a class only for “I don’t know” largely contributed to these differences. Many independent components (ICs) that exhibited high accuracy in “Yes + No” vs. “I don’t know” response classification were associated with activation of areas from the frontal to parietal lobes, while many ICs that showed high accuracy in the “Yes vs. No” classification were associated with activation of an area ranging from the parietal to the occipital lobes and were more broadly localized in cortical regions than was seen for the “Yes + No” vs. “I don’t know” classification. Our results suggest that small and large delays in real-time cursor control differ not only in the magnitude of the delay but should be handled as distinct information in different ways and might involve differential processing in the brain.

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Decoding finger movement in humans using synergy of EEG cortical current signals

Cited by 30 publications

References 58 publications

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

Investigation of Delayed Response during Real-Time Cursor Control Using Electroencephalography

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