Quantum inspired feature engineering for explainable EEG signal classification.

In this research, our main objective is to extract more informative features by deploying a simple and effective framework. One of the cheapest data-gathering methods from the brain is electroencephalography signal collection. The main aim of this approach is to obtain maximum information from electroencephalography signals. Therefore, we have presented a quantum-inspired feature extraction function and evaluated its classification ability. In this approach, we have employed six electroencephalography signal datasets as a testbed, aiming to depict the general classification capability of the introduced electroencephalography signal classification model. Firstly, a quantum entangled particle pattern has been proposed, which is a transformer-based feature extraction function. To investigate the classification performance of the introduced quantum entangled particle pattern, a new-generation explainable feature engineering framework has been introduced. The quantum entangled particle pattern-centric explainable feature engineering model extracts features using the quantum entangled particle pattern feature extraction function. By employing cumulative weighted iterative neighborhood component analysis, the most distinctive features extracted by quantum entangled particle pattern have been selected. The algorithm-centric k-nearest neighbor classifier has been applied to obtain classification results. Directed lobish has been utilized to generate interpretable results. To obtain both classification and interpretable results, the selected features and their identities have been used as inputs for centric k-nearest neighbors and directed lobish consecutively. The introduced quantum entangled particle pattern-related explainable feature engineering approach attained over 90% classification accuracy on the six electroencephalography signal datasets with 10-fold cross-validation. Additionally, this model generates a connectome diagram to provide interpretable results for each dataset.