Radial Basis Function Neural Network for Classification of Quantitative EEG in Patients with Advanced Chronic Renal Failure

Barios, Juan A.; Gonzalez, Cesar A.; Benbunan, Bettina; Fernández-Armayor, Victor; Teruel, J.L.; Fernandez, M Sanmartin; Pedrera, A.; Gaztelu, J.M.

doi:10.1007/978-3-642-21344-1_43

Cited by 1 publication

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“…It was shown that while the use of linear SVM with spatial and temporal principal component analysis (PCA) demonstrated 73% accuracy [40], RBF-SVM allowed to reach up to 93% accuracy in combination with independent component analysis (ICA) [41] and 81% in combination with genetic algorithm (GA) [42,43]. The radial basis function (RBF) neural network architecture was applied by Barios et al [44] to classify patients with chronic renal failure and demonstrated 86.6% accuracy without optimization. Later, Pei et al [45] with improved RBF network demonstrated 87.14% accuracy in classification of left and right hand motor imagery tasks.…”

Section: Discussionmentioning

confidence: 99%

Artificial Neural Network Classification of Motor‐Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

et al. 2018

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We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves than in the high-frequency beta brainwave (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

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