MEG based classification of wrist movement

Montazeri, N.; Shamsollahi, Mohammad Bagher; Hajipour, Sepideh

doi:10.1109/iembs.2009.5334472

Cited by 7 publications

(10 citation statements)

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“…Our results are consistent with their findings. The relative rate of ripple frequency oscillations is an interesting potential biomarker for the epileptic neocortex, but larger prospective studies correlating HFOs rates with seizure-onset zones, resected tissue and surgical outcomes are required to determine the true predictive value of this line of research (Montazeri et al, 2009; Blanco et al, 2011; Worrell et al, 2012). However, to our understanding, algorithmic requirements differ substantially for data mining and for topological (feature) data analysis.…”

Section: Discussionmentioning

confidence: 99%

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Xiang

Luo

Kotecha

et al. 2014

Front. Neuroinform.

129

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Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation's memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (1~70 Hz) and high-frequency (70~200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 h by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2–3 days and used 1–2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.

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Section: Discussionmentioning

confidence: 99%

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Xiang

Luo

Kotecha

et al. 2014

Front. Neuroinform.

129

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“…Decoding brain states based on MEG has been extensively studied over the past decade [4]- [8]. A number of machine learning techniques, such as linear discriminant analysis (LDA) [5] and support vector machine (SVM) [6], have been applied for MEG decoding. Most of these decoding algorithms aim to identify the optimal discriminant pattern that distinguishes different brain states with maximal accuracy.…”

Section: Introductionmentioning

confidence: 99%

Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions

Zhang

Foldes

et al. 2016

IEEE Trans. Biomed. Eng.

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Brain state decoding based on whole-head MEG has been extensively studied over the past decade. Recent MEG applications pose an emerging need of decoding brain states based on MEG signals originating from prespecified cortical regions. Toward this goal, we propose a novel region-of-interest-constrained discriminant analysis algorithm (RDA) in this paper. RDA integrates linear classification and beamspace transformation into a unified framework by formulating a constrained optimization problem. Our experimental results based on human subjects demonstrate that RDA can efficiently extract the discriminant pattern from prespecified cortical regions to accurately distinguish different brain states.

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“…Montazeri et al, [20] proposed a method for decoding of wrist movement from directionally modulated MEG signals using a cross domain feature space, LDA and SVM for classification. This is evaluated on BCI competition IV data same as that evaluated in our work.…”

Section: Related Workmentioning

confidence: 99%

“…TABLE VII. LEADER BOARD OF THE BCI COMPETITION IV [22] No. Accuracy (%) Laboratory S1 S2 Montazeri et al, [20] in their work have published the results corresponding to the maximum achievable prediction accuracy and that achieved by their proposed method. This method involves a cross-domain feature extraction method that extracts wavelet coefficients and form factor from the training data, these features extracted are further normalized and LDA is used for feature selection followed by Principal Component Analysis (PCA).…”

Section: Algorithmmentioning

confidence: 99%

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Efficient MEG signal decoding of direction in wrist movement using curve fitting (EMDC)

Krishna

Vinay

Raja

2011

2011 International Conference on Image Information Processing

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Magnetoencephalography (MEG) can be used as an effective non-invasive interface with the brain to provide movement-related information similar to invasive signal recordings. This paper proposes a reliable and efficient algorithm for classification of wrist movement in four directions from MEG signals of two subjects. Our approach involves signal smoothing, design of a class-specific Unique Identifier Signal (UIS) and curve fitting to identify the direction in a given test signal. Our algorithm is evaluated with the data set provided in BCI competition 2008. Our simulations show the best average prediction accuracy of 88.84% for this four-class classification problem. The results of the proposed model are found to be superior to most other techniques in vogue.

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MEG based classification of wrist movement

Cited by 7 publications

References 0 publications

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions

Efficient MEG signal decoding of direction in wrist movement using curve fitting (EMDC)

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