Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods

Tobar, Alejandra Mejia; Hyoudou, Rikiya; Kita, Kiyoshi; Nakamura, Takenobu; Kambara, Hiroyuki; Ogata, Yuji; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

doi:10.3389/fnins.2017.00733

Cited by 8 publications

(9 citation statements)

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“…VBMEG was originally developed to perform a hierarchical Bayesian source current estimation proposed by Sato et al (2004), and the first version was released in 2011 (https://vbmeg.atr.jp/). Its reliability was confirmed in various studies by our group (Yoshioka et al, 2008; Callan et al, 2010; Aihara et al, 2012; Takeda et al, 2014) and others (Toda et al, 2011; Yoshimura et al, 2012, 2017; Yamagishi and Anderson, 2013; Morioka et al, 2014; Callan et al, 2016; Ohata et al, 2016; Yanagisawa et al, 2016; Fukuma et al, 2018; Mejia et al, 2018; Sato et al, 2018). Recently, VBMEG was extended to perform a connectome dynamics estimation proposed by Fukushima et al (2015), and its second version was released in 2017.…”

Section: Introductionmentioning

confidence: 72%

MEG Source Imaging and Group Analysis Using VBMEG

et al. 2019

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Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) ( https://vbmeg.atr.jp/ ). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page ( https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html ), we show its full pipeline using an open dataset recorded by Wakeman and Henson ( 2015 ). We import the MEG data and preprocess them to estimate the source currents. From the estimated source currents, we perform a group analysis and examine the differences of current amplitudes between conditions by controlling the false discovery rate (FDR), which yields results consistent with previous studies. We highlight VBMEG's characteristics by comparing these results with those obtained by other source imaging methods: weighted minimum norm estimate (wMNE), dynamic statistical parametric mapping (dSPM), and linearly constrained minimum variance (LCMV) beamformer. We also estimate source currents from the EEG data and the whole-brain connectome dynamics from the MEG data and dMRI. The observed results indicate the reliability, characteristics, and usefulness of VBMEG.

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Section: Introductionmentioning

confidence: 72%

MEG Source Imaging and Group Analysis Using VBMEG

et al. 2019

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“…Bulea et al ( 2014 ) used the EMG with EEG to increase the classification accuracy by recording EMG data from lower extremity muscular movements and the onset of each stand-to-sit and sit-to-stand transition. Tobar et al ( 2018 ) used the EMG electrode to ensure task execution during decoding of ankle flexion and extension.…”

Section: Hybrid Eeg-fnirs-based Bcimentioning

confidence: 99%

“…It was found that hybridization helped to increase the classification accuracy from 79.4 to 88.5%. Tobar et al ( 2018 ) used fMRI with EEG and EMG to identify the exact location of brain area activation. Figure 5 proposed possible brain imaging and sensor placement to better understand human kinematics and dynamics for future works.…”

Section: Hybrid Eeg-fnirs-based Bcimentioning

confidence: 99%

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Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Khan

Naseer

Yazidi

et al. 2021

Front. Hum. Neurosci.

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Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

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“…Currently, there are two brain-computer interface technologies, invasive and non-invasive. Non-invasive BCI is widely used because of its convenient operation and low cost [ 5 , 6 ]. Through the non-invasive BCI, we can obtain various patterns of brain activity signals, which are extensively studied and used in signal processing, pattern recognition, cognitive science, medicine, rehabilitation and other fields [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Zheng

Wang

et al. 2020

Sensors

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Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.

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Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods

Cited by 8 publications

References 50 publications

MEG Source Imaging and Group Analysis Using VBMEG

MEG Source Imaging and Group Analysis Using VBMEG

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

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