Pattern Recognition of Motor Imagery EEG using Wavelet Transform

Bao-guo, XU; Song, Aiguo

doi:10.4236/jbise.2008.11010

Cited by 72 publications

(31 citation statements)

References 4 publications

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“…In [14], a P-300 based BCI was proposed using descriptive statistics from the different levels of a CWT. This scheme is also typical in motor imagery BCIs, as in [15] where a DWT was used in the same manner.…”

Section: Multiresolution Analysis Over Simple Graphs For Brain Computmentioning

confidence: 99%

Multiresolution analysis over simple graphs for brain computer interfaces

2013

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Abstract. Objective: Multiresolution analysis (MRA) offers a useful framework for signal analysis in the temporal and spectral domains, although commonly employed MRA methods such as Daubechies wavelets may not be the best approach for brain computer interface (BCI) applications. Approach: Hereby we propose the use of a lifting scheme transform over graphs and a tailored simple graph representation for EEG data which results on a MRA system where temporal, spectral and spatial characteristics are used to extract motor imagery features. The transformed data is processed within a simple experimental framework to test the classification performance. Main Results: The proposed method can significantly improve the classification results obtained by various wavelet families using the same methodology. Preliminary results using common spatial patterns as feature extraction method show that we can achieve comparable classification accuracy to more sophisticated methodologies. From the analysis of the results we can obtain insights into the pattern development in the EEG data, which provide useful information for feature basis selection and thus for improving classification performance. Significance: Applying wavelet lifting over graphs is a new approach for handling BCI data. The inherent flexibility of the lifting scheme could lead to new approaches based on the proposed method which could further improve the classification performance here presented.

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Section: Multiresolution Analysis Over Simple Graphs For Brain Computmentioning

confidence: 99%

Multiresolution analysis over simple graphs for brain computer interfaces

2013

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“…where f(t), w s ð Þ, a and s are the input function, mother wavelet, scale parameter, and shift parameter, respectively (Xu and Song 2008;Nakatani et al 2011). Then, the wavelet energy as a function of the frequency is the squared absolute of CWT.…”

Section: Short Time Fourier Transform (Stft)mentioning

confidence: 99%

“…In this study, CWT is applied to the data in feature extraction part. The relation corresponding to CWT is given by the following equation (Wang et al 2009;Xu and Song 2008):…”

Section: Short Time Fourier Transform (Stft)mentioning

confidence: 99%

Evaluation of local field potential signals in decoding of visual attention

Seif

Daliri

2015

Cogn Neurodyn

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In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time-frequency domain.

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“…It is designed as a two-level scheme, namely, the high level and low level. The feature extraction and classification algorithm 27 of motor imagery EEG is implemented by the high-level controller running on the master PC. Position control of WAM Arm is realized by the lowlevel controller running on the slave PC.…”

Section: Controller Designmentioning

confidence: 99%

Robotic neurorehabilitation system design for stroke patients

Song

Zhao³

et al. 2015

Advances in Mechanical Engineering

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In this article, a neurorehabilitation system combining robot-aided rehabilitation with motor imagery-based brain-computer interface is presented. Feature extraction and classification algorithm for the motor imagery electroencephalography is implemented under our brain-computer interface research platform. The main hardware platform for functional recovery therapy is the Barrett Whole-Arm Manipulator. The mental imagination of upper limb movements is translated to trigger the Barrett Whole-Arm Manipulator Arm to stretch the affected upper limb to move along the predefined trajectory. A fuzzy proportional-derivative position controller is proposed to control the Whole-Arm Manipulator Arm to perform passive rehabilitation training effectively. A preliminary experiment aimed at testing the proposed system and gaining insight into the potential of motor imagery electroencephalography-triggered robotic therapy is reported.

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Pattern Recognition of Motor Imagery EEG using Wavelet Transform

Cited by 72 publications

References 4 publications

Multiresolution analysis over simple graphs for brain computer interfaces

Multiresolution analysis over simple graphs for brain computer interfaces

Evaluation of local field potential signals in decoding of visual attention

Robotic neurorehabilitation system design for stroke patients

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