Classification of Motor Imagery EEG Signals Based on Time–Frequency Analysis

Mu,

doi:10.4156/jdcta.vol3.issue4.13

Cited by 20 publications

(5 citation statements)

References 18 publications

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“…Thus, the one-dimensional feature in the time domain is expanded to two-dimensional features in the time-frequency domain. Past studies have shown that the frequency feature plays an important role in BCI detection [14] , [35] , [43] , [44] . Our method expands time features to time and frequency features, allowing more feature vectors to be used in feature detection.…”

Section: Discussionmentioning

confidence: 99%

Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

Wang

2014

PLoS ONE

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With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.

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Section: Discussionmentioning

confidence: 99%

Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

Wang

2014

PLoS ONE

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“…The choice of filter is possible for sharpest roll-off and equi-ripple in pass and stop band that can be varied accordingly. The pass band frequency of the filter is selected due to the presence of motor imagery signals in theta (3-7 Hz), alpha (7-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [21] frequency bands.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Last is the selection of a special type of recurrent neural network, where the correct intended classes represent the stable points on the minima of Lyapunov surface designed for the neural network. The choice of the neural network classifier is made because of extensive use in motor imagery classification [17], [18] and of course our background [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Decoding of motor imagery potentials in driving using DE-induced fuzzy-neural classifier

Saha

Konar

Dan

et al. 2015

2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)

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This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90º left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent neural network classifier offers better performance using proposed differential evolution-induced feature selection technique, when compared with principal component analysis in such situation and provides the highest classification accuracy of 98.472%. In addition, false classification rate/misclassification rate is also found much higher when using principal component analysis instead of proposed differential evolution-induced feature selection algorithm. The performance of the proposed differential evolution-induced fuzzy recurrent neural network classifier has been compared with a list of standard classifiers including linear support vector machines, k-nearest neighbor and support vector machines with radial basis function kernel, where fuzzyrecurrent neural network classifier outperforms its competitors with an average classification accuracy of 95.472% and 95.647 for steering left and acceleration motor intensions respectively.

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“…While multi-channel electroencephalogram (EEG) signals offer enhanced spatial resolution [12], they also introduce unwanted noise interference and redundant information [13]. An effective channel selection method can mitigate this issue by eliminating channels containing noise signals and retaining those correlated with the activation patterns of specific brain regions [14].…”

Section: Introductionmentioning

confidence: 99%

An EEG channel selection method for motor imagery based on Fisher score and local optimization

Luo,

Mu,

Wang

et al. 2024

J. Neural Eng.

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Objective. Multi-channel electroencephalogram (EEG) technology in brain–computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness. Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection. Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%. Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.

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Classification of Motor Imagery EEG Signals Based on Time–Frequency Analysis

Cited by 20 publications

References 18 publications

Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

Decoding of motor imagery potentials in driving using DE-induced fuzzy-neural classifier

An EEG channel selection method for motor imagery based on Fisher score and local optimization

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