Automated mental arithmetic performance detection using quantum pattern- and triangle pooling techniques with EEG signals

Background: Electroencephalography (EEG) signals recorded during mental arithmetic tasks can be used to quantify mental performance. The classification of these input EEG signals can be automated using machine learning models. We aimed to develop an efficient handcrafted model that could accurately discriminate bad counters vs. good counters in mental arithmetic. Materials and method: We studied a public mental arithmetic task performance EEG dataset comprising 20-channel EEG signal segments recorded from 36 healthy right-handed subjects divided into two classes 10 bad counters and 26 good counters. The original 60-second EEG samples are divided into 424 15-second segments (119 and 305 belonging to the bad counters and good counters, respectively) to input into our model. Our model comprised a novel multilevel feature extraction method based on (1) four rhombuses lattice pattern, a new generation function for feature extraction that was inspired by the lattice structure in post-quantum cryptography; and (2) triangle pooling, a new distance-based pooling function for signal decomposition. These were combined with downstream feature selection using iterative neighborhood component analysis, channel-wise result classification using support vector machine with leave-one-subject-out (LOSO) and 10-fold) crossvalidations (CVs) to calculate prediction vectors, iterative majority voting to generate voted vectors, and greedy algorithm to obtain the best results. Results: The model attained 88.44% and 96.42% geometric means and accuracies of 93.40% and 97.88%, using LOSO and 10-fold CVs, respectively. Conclusions: Our model's >93% classification accuracies compared favorably against published literature. Importantly, the model has linear computational complexity, which enhances its ease of implementation.

Automated summary

We developed a machine learning model to quantify mental performance in mental arithmetic tasks, which accurately discriminated between bad counters and good counters. Our model used a novel multilevel feature extraction method and a distance-based pooling function for signal decomposition, along with feature selection and result classification algorithms. The model achieved high classification accuracies in both cross-validations, showing its effectiveness in mental arithmetic tasks.