Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices

Novel accurate classification system developed using order transition pattern feature engineering technique with physiological signals

This paper presents a novel, explainable feature engineering framework for classifying EEG and ECG signals with high accuracy. The proposed method employs the Order Transition Pattern (OTPat) feature extractor. The presented OTPat feature extractor captures both channel/column-based patterns (spatial features) using all channels for each point and signal/row-based patterns (temporal features) by extracting features from individual channels using overlapping blocks. The extracted features are then refined using cumulative weighted iterative neighborhood component analysis (CWINCA) for feature selection and classified with a t‑algorithm k‑nearest neighbors (tkNN) classifier. Finally, two symbolic languages, Directed Lobish (DLob) and Cardioish, generate interpretable results in the form of cortical and cardiac connectome diagrams. The OTPat-based XFE model achieves over 95% accuracy on several EEG and ECG datasets and reaches 86.07% accuracy on an 8‑class EEG artifact dataset. These results demonstrate high performance and clear interpretability, highlighting the model's potential for robust biomedical signal classification.

Wearable electroencephalography and multi-modal mental state classification: A systematic literature review

BACKGROUND

Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent.

METHOD

Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification.

RESULTS

Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed.

CONCLUSIONS

Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.