Identification of three mental states using a motor imagery based brain machine interface

Jiralerspong, Trongmun; Liu, Chao; Ishikawa, Jun

doi:10.1109/cibci.2014.7007792

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…From the 14 electrodes that the Emotiv-Epoc device provides, we decided to perform an analysis on the electrodes located in three different regions of the cerebral cortex looking for those with better performance to form the quaternion: (1) electrodes located on the motor cortex, which generates neural impulses that control movements, (2) those on the posterior parietal cortex, where visual information is transformed into motor instructions [ 51 ], and (3) the ones on the prefrontal cortex, which appear as a marker of the anticipations that the body must make to adapt to what is going to happen immediately after [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

Batres-Mendoza

Ibarra-Manzano

Guerra-Hernandez

et al. 2017

Computational Intelligence and Neuroscience

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We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications.

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Section: Methodsmentioning

confidence: 99%

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

Batres-Mendoza

Ibarra-Manzano

Guerra-Hernandez

et al. 2017

Computational Intelligence and Neuroscience

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show abstract

“…Similarly, Bhattacharyya [ 22 ] conducted a study of comparative performance analysis of different classifiers, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), linear SVM, radial basis function (RBF) SVM and naïve Bayesian) to differentiate EEG signals for left-right limb movement, with SVM being the most accurate at 82.14%. In turn, Jiralerspong [ 23 ] conducted an experimental test of three mental states using FFT with a hamming window function that resulted in a 72% recognition rate. These strategies show good accuracy rates, but the information is extracted within a frequency or time-frequency domain, thereby losing vital information.…”

Section: Introductionmentioning

confidence: 99%

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

Batres-Mendoza

Montoro-Sanjose

Guerra-Hernandez

et al. 2016

Sensors

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Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states.

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EEG-Based Person Identification Using Rhythmic Brain Activity During Sleep

Koutras

Kostopoulos

2018

Artificial Neural Networks and Machine Learning – ICANN 2018

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Identification of three mental states using a motor imagery based brain machine interface

Cited by 5 publications

References 31 publications

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

EEG-Based Person Identification Using Rhythmic Brain Activity During Sleep

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