Key result
The CT-LS-SVM hybrid approach achieved an average classification accuracy of 99.19% for mental imagery tasks and 94.18% for epileptic EEG data, outperforming previously reported algorithms.
Why the study?
Does the CT-LS-SVM hybrid approach improve classification accuracy for two-class EEG signals compared to previous methods?
Does the CT-LS-SVM hybrid approach improve classification accuracy for two-class EEG signals compared to previous methods?
The CT-LS-SVM hybrid approach demonstrates high classification accuracy for epileptic and mental imagery EEG signals, outperforming previously reported algorithms.
Hypothesis-generating for EEG classification in epilepsy and brain-computer interfaces; prospective clinical validation required before adoption.
This paper presents a novel hybrid approach based on clustering technique (CT) and least square support vector machine (LS-SVM) denoted as CT-LS-SVM for classifying two-class EEG signals. The study aims to extract representative features from the original EEG data through the CT method and then to classify two-class EEG signals by the LS-SVM using these features as inputs. In order to test the effectiveness of the proposed method, the experiment is carried out on an epileptic EEG data and a mental imagery tasks EEG data. The classification accuracy of the current method is compared to the previous reported methods of the literature. The proposed approach is found to achieve an average classification accuracy of 99.19% for the mental imagery tasks EEG data and 94.18% for the epileptic EEG data. Our results show the highest classification accuracy (99.90%) for healthy subjects with eyes open (Set A) and epileptic patients during seizure activity (Set E) from the epileptic EEG data among the reported algorithms. Thus, the findings of the current research demonstrate that the CT method is efficient for extracting features representing the EEG signals and the LS-SVM classifier has the inherent ability to solve a pattern recognition task for these features.
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Siuly et al. (2010) studied Epilepsy and mental imagery tasks. CT-LS-SVM (hybrid clustering technique and least square support vector machine) vs. Previously reported methods was evaluated on Classification accuracy. The CT-LS-SVM hybrid approach achieved an average classification accuracy of 99.19% for mental imagery tasks and 94.18% for epileptic EEG data, outperforming previously reported algorithms.
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