Structural Plasticity (SP) in the brain is a process that allows neuronal structure changes, in response to learning. Spiking Neural Networks (SNN) are an emerging form of artificial neural networks that uses brain-inspired techniques to learn. However, the application of SP in SNNs, its impact on overall learning and network behaviour is rarely explored. In the present study, we use an SNN with a single hidden layer, to apply SP in classifying Electroencephalography signals of two publicly available datasets. We considered classification accuracy as the learning capability and applied metaheuristics to derive the optimised number of neurons for the hidden layer along with other hyperparameters of the network. The optimised structure was then compared with overgrown and undergrown structures to compare the accuracy, stability, and behaviour of the network properties. Networks with SP yielded ~94% and ~92% accuracies in classifying wrist positions and mental states(stressed vs relaxed) respectively. The same SNN developed for mental state classification produced ~77% and ~73% accuracies in classifying arousal and valence. Moreover, the networks with SP demonstrated superior performance stability during iterative random initiations. Interestingly, these networks had a smaller number of inactive neurons and a preference for lowered neuron firing thresholds. This research highlights the importance of systematically selecting the hidden layer neurons over arbitrary settings, particularly for SNNs using Spike Time Dependent Plasticity learning and provides potential findings that may lead to the development of SP learning algorithms for SNNs.
Objective: This study examined the data modeling capability of spiking neural networks (SNN) in classifying stressed versus relaxed brain states using electroencephalogram (EEG) data. The input spatiotemporal dynamics were explored to obtain further knowledge regarding the two-brain states. Method: A publicly available EEG data set for emotion analysis using psychological signals (DEAP) collected from 32 participants (50% females) with an average age of 26.9 is used in this study. Firstly, data extraction is performed using a criterion that defines stress and relaxation states using self-reported valence and arousal scores. Two hundred eight such extracted samples were selected to train and evaluate a novel three-layer feedforward SNN. This SNN consisted of leaky-integrate and fire neurons and learned from incoming data using spike-time-dependent plasticity (STDP) and dynamically evolving SNN algorithms. The SNN performance was evaluated using both fivefold cross-validation and a 60:40 training testing split. To explore input spatiotemporal dynamics, a specialized SNN architecture for brain data processing named NeuCube was used. Results: The highest-performing model of the novel SNN algorithm produced 88% average accuracy (F1 score: 86.21%, Matthews correlation coefficient: 0.78). This SNN outperformed traditional machine learning (ML) techniques without the use of manual feature extraction. Moreover, the input dynamics revealed higher prefrontal activation during relaxation states compared to stress states. Conclusions: The results showed the capability of the SNN algorithm to recognize stressed and relaxed states of the brain, using temporal learning techniques. Furthermore, the findings obtained from NeuCube suggested a potential approach for brain data analysis, setting SNNs apart from black box approaches used for brain data processing.
Mental stress is found to be strongly connected with human cognition and wellbeing. As the complexities of human life increase, the effects of mental stress have impacted human health and cognitive performance across the globe. This highlights the need for effective non-invasive stress detection methods. In this work we introduce a novel, artificial spiking neural network model called, Online Neuroplasticity Spiking Neural Network (O-NSNN) that utilizes a repertoire of learning concepts inspired by the brain, to classify mental stress using Electroencephalogram (EEG) data. These models are personalized and tested on EEG data recorded during sessions in which participants listen to different types of narratives designed to induce acute stress. Our O-NSNN models learn on the fly producing an average accuracy of 90.76% (σ = 2.09) when classifying EEG signals of brain states associated with these audio comments. The brain-inspired nature of the individual models makes them robust and efficient and has the potential to be integrated into wearable technology. Furthermore, this article presents an exploratory analysis of trained O-NSNNs to discover links between perceived and acute mental stress. The O-NSNN algorithm proved to be better for personalized stress recognition in terms of accuracy, efficiency, and model interpretability.
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