Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks

Morales-Flores, Emmanuel; Ramírez-Cortés, Juan Manuel; Gómez-Gil, Pilar; Alarcón-Aquino, Vicente

doi:10.1007/978-3-642-33021-6_11

Cited by 1 publication

(2 citation statements)

References 29 publications

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“…This method provides fully automated detection and quantification of ERP components that best discriminate between two samples of EEG signals. A new method is also proposed in [19] [20], Self organizing Feature Map [21], Multiple Kernel Learning Support Vector Machine [22], neural networks with improved particle swarm optimization [23], recurrent neural networks [24], time-frequency analysis [25], Fuzzy sets based classification [26] and Independent Component Analysis (ICA) [27]. This paper presents a novel but a very simple method to effectively classify the EEG of mental tasks for left-hand movement imagination, right-hand movement imagination and word generation.…”

Section: Imagination Of Movements Is Called Sensory Motor Rhythm (Smrmentioning

confidence: 99%

“…As already said, we have used a simple MLP to achieve the demonstrated results as against the most complex classifiers used in [20][21][22][23][24][25][26][27]. Feed forward neural network like Multi layer Perceptron (MLP) with different variants of back propagation learning algorithms assisted by CLPSO are used to achieve better classification.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

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Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

Bawane¹,

Bhurchandi²

2016

IJCA

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With recent advances in Electroencephalogram (EEG) signal processing and biomedical instrumentation, brain machine interfaces are used for rehabilitation of people suffering from neuromuscular disorders. This paper presents a novel method employing Hierarchical classifier using optimised Neural Networks to classify left-hand movement, right-hand movement and word generation using EEG signals. One of the most important components of brain computer interface (BCI) is feature extraction of EEG signals. Power spectral density (PSD) is used for feature extraction from EEG signals. The proposed pre-processing and reconfiguration of PSD samples make them more discriminative & yield appropriately organized feature vectors. The adaptation of network weights using Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed to improve the performance of Neural Network (NN). Further, the two level hierarchical neural network is used to enhance the discriminative property of the features and hence better classification accuracy is achieved. Results are verified on BCI benchmarking database as well as our own experimental database. Results obtained using the proposed methods are compared with other contemporary methods such as Linear Discrimination analysis (LDA), neural networks based on improved particle swarm optimization (IPSONN) and to a recently proposed approach based on Evidence-based combining classifier. It is found that the proposed method outperforms all the contemporary techniques for the multi-task EEG classification. This new method can be easily extended to other multitask BCI applications.

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Section: Imagination Of Movements Is Called Sensory Motor Rhythm (Smrmentioning

confidence: 99%

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

Bawane¹,

Bhurchandi²

2016

IJCA

View full text Add to dashboard Cite

show abstract

Mental Tasks Temporal Classification Using an Architecture Based on ANFIS and Recurrent Neural Networks

Cited by 1 publication

References 29 publications

Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

Classification of Mental Tasks using EEG and Hierarchical Classifier employing Optimised Neural Networks

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