Evolving Connectionist Systems: The Knowledge Engineering Approach
Evolving Connectionist Systems: The Knowledge Engineering Approach
Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence
EEG Based Biometric Framework for Automatic Identity Verification
Journal of VLSI Signal Processing Systems
Real-time epileptic seizure detection on intra-cranial rat data using reservoir computing
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Towards spatio-temporal pattern recognition using evolving spiking neural networks
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots
IEEE Transactions on Autonomous Mental Development
Collective stability of networks of winner-take-all circuits
Neural Computation
Probabilistic Computational Neurogenetic Modeling: From Cognitive Systems to Alzheimer's Disease
IEEE Transactions on Autonomous Mental Development
NeuCube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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This study investigates the feasibility of Bens Spike Algorithm (BSA) to encode continuous EEG spatio-temporal data into input spike streams for a classification in a spiking neural network classifier. A novel evolving probabilistic spiking neural network reservoir (epSNNr) architecture is used for the purpose of learning and classifying the EEG signals after the BSA transformation. Experiments are conducted with EEG data measuring a cognitive state of a single individual under 4 different stimuli. A comparison is drawn between using traditional machine learning algorithms and using BSA plus epSNNr, when different probabilistic models of neurons are utilised. The comparison demonstrates that: (1) The BSA is a suitable transformation for EEG data into spike trains; (2) The performance of the epSNNr improves when a probabilistic model of a neuron is used, compared to the use of a deterministic LIF model of a neuron; (3) The classification accuracy of the EEG data in an epSNNr depends on the type of the probabilistic neuronal model used. The results suggest that an epSNNr can be optimised in terms of neuronal models used and parameters that would better match the noise and the dynamics of EEG data. Potential applications of the proposed method for BCI and medical studies are briefly discussed.