An introduction to genetic algorithms
An introduction to genetic algorithms
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
ROCR: visualizing classifier performance in R
Bioinformatics
Neural Networks in a Softcomputing Framework
Neural Networks in a Softcomputing Framework
Introduction to Statistical Methods and Data Analysis (with CD-ROM)
Introduction to Statistical Methods and Data Analysis (with CD-ROM)
Robust EEG channel selection across subjects for brain-computer interfaces
EURASIP Journal on Applied Signal Processing
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
Neural Networks and Computing: Learning Algorithms and Applications
Neural Networks and Computing: Learning Algorithms and Applications
A genetic algorithm that adaptively mutates and never revisits
IEEE Transactions on Evolutionary Computation
Subject and class specific frequency bands selection for multiclass motor imagery classification
International Journal of Imaging Systems and Technology
The problem of bias in training data in regression problems in medical decision support
Artificial Intelligence in Medicine
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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Objective: An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in BCI research and have numerous successful applications. However, one of the drawbacks of conventional ANNs is the lack of an explicit input optimization mechanism. In addition, results of ANN learning are usually not easily interpretable. In this paper, we have applied an ANN-based method, the genetic neural mathematic method (GNMM), to two EEG channel selection and classification problems, aiming to address the issues above. Methods and materials: Pre-processing steps include: least-square (LS) approximation to determine the overall signal increase/decrease rate; locally weighted polynomial regression (Loess) and fast Fourier transform (FFT) to smooth the signals to determine the signal strength and variations. The GNMM method consists of three successive steps: (1) a genetic algorithm-based (GA-based) input selection process; (2) multi-layer perceptron-based (MLP-based) modelling; and (3) rule extraction based upon successful training. The fitness function used in the GA is the training error when an MLP is trained for a limited number of epochs. By averaging the appearance of a particular channel in the winning chromosome over several runs, we were able to minimize the error due to randomness and to obtain an energy distribution around the scalp. In the second step, a threshold was used to select a subset of channels to be fed into an MLP, which performed modelling with a large number of iterations, thus fine-tuning the input/output relationship. Upon successful training, neurons in the input layer are divided into four sub-spaces to produce if-then rules (step 3). Two datasets were used as case studies to perform three classifications. The first data were electrocorticography (ECoG) recordings that have been used in the BCI competition III. The data belonged to two categories, imagined movements of either a finger or the tongue. The data were recorded using an 8x8 ECoG platinum electrode grid at a sampling rate of 1000Hz for a total of 378 trials. The second dataset consisted of a 32-channel, 256Hz EEG recording of 960 trials where participants had to execute a left- or right-hand button-press in response to left- or right-pointing arrow stimuli. The data were used to classify correct/incorrect responses and left/right hand movements. Results: For the first dataset, 100 samples were reserved for testing, and those remaining were for training and validation with a ratio of 90%:10% using K-fold cross-validation. Using the top 10 channels selected by GNMM, we achieved a classification accuracy of 0.80+/-0.04 for the testing dataset, which compares favourably with results reported in the literature. For the second case, we performed multi-time-windows pre-processing over a single trial. By selecting 6 channels out of 32, we were able to achieve a classification accuracy of about 0.86 for the response correctness classification and 0.82 for the actual responding hand classification, respectively. Furthermore, 139 regression rules were identified after training was completed. Conclusions: We demonstrate that GNMM is able to perform effective channel selections/reductions, which not only reduces the difficulty of data collection, but also greatly improves the generalization of the classifier. An important step that affects the effectiveness of GNMM is the pre-processing method. In this paper, we also highlight the importance of choosing an appropriate time window position.