Brain Computer Interface issues on hand movement

Pattnaik, Prasant Kumar; Sarraf, Jay

doi:10.1016/j.jksuci.2016.09.006

Cited by 38 publications

(31 citation statements)

References 14 publications

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“…Human EEG signals were sampled with a length of N = 16,384 data points with eyes closed during random hand movements at 500 Hz using a Neurofax EEG System (see Acknowledgment; Pattnaik and Sarraf, in press). These empirical records were acquired from healthy volunteers above the pre-frontal area.…”

Section: Methodsmentioning

confidence: 99%

Decomposing Multifractal Crossovers

et al. 2017

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Physiological processes—such as, the brain's resting-state electrical activity or hemodynamic fluctuations—exhibit scale-free temporal structuring. However, impacts common in biological systems such as, noise, multiple signal generators, or filtering by transport function, result in multimodal scaling that cannot be reliably assessed by standard analytical tools that assume unimodal scaling. Here, we present two methods to identify breakpoints or crossovers in multimodal multifractal scaling functions. These methods incorporate the robust iterative fitting approach of the focus-based multifractal formalism (FMF). The first approach (moment-wise scaling range adaptivity) allows for a breakpoint-based adaptive treatment that analyzes segregated scale-invariant ranges. The second method (scaling function decomposition method, SFD) is a crossover-based design aimed at decomposing signal constituents from multimodal scaling functions resulting from signal addition or co-sampling, such as, contamination by uncorrelated fractals. We demonstrated that these methods could handle multimodal, mono- or multifractal, and exact or empirical signals alike. Their precision was numerically characterized on ideal signals, and a robust performance was demonstrated on exemplary empirical signals capturing resting-state brain dynamics by near infrared spectroscopy (NIRS), electroencephalography (EEG), and blood oxygen level-dependent functional magnetic resonance imaging (fMRI-BOLD). The NIRS and fMRI-BOLD low-frequency fluctuations were dominated by a multifractal component over an underlying biologically relevant random noise, thus forming a bimodal signal. The crossover between the EEG signal components was found at the boundary between the δ and θ bands, suggesting an independent generator for the multifractal δ rhythm. The robust implementation of the SFD method should be regarded as essential in the seamless processing of large volumes of bimodal fMRI-BOLD imaging data for the topology of multifractal metrics free of the masking effect of the underlying random noise.

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Section: Methodsmentioning

confidence: 99%

Decomposing Multifractal Crossovers

et al. 2017

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“…First, the narrow window and its combination in tackling non-stationary EEG signal [24]. Second, the using of higher order statistic (skewness and kurtosis) as statistical feature extraction method [6], mean average value and root mean square [22], [34]. Finally, the using of channel instantiation approach [22] that create more instance that effective for k-NN as instance-based classifier in EEG based MI classification [6].…”

Section: B Selected Channel Classification Resultsmentioning

confidence: 99%

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Wijaya¹,

Adji²,

Setiawan³

2018

EECSI

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Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems.

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“…We strongly recommend reading several complete survey papers [82,85,[174][175][176][177][178][179][180] that provide the state of the art on brain-machine interface applications and a detail focus on definitions, classifications and comparisons of the brain signals. In addition, they survey the current brain interface hardware and software and explain the current challenges of the field.…”

Section: Brain and Body Machine Interfacementioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

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Brain Computer Interface issues on hand movement

Cited by 38 publications

References 14 publications

Decomposing Multifractal Crossovers

Decomposing Multifractal Crossovers

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Deep Learning in the Biomedical Applications: Recent and Future Status

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