Assessment of Mental Workload Using a Transformer Network and Two Prefrontal EEG Channels: An Unparameterized Approach

Despite promising results reported in the literature for mental workload assessment using electroencephalography (EEG), most of the proposed methods rely on employing multiple EEG channels, limiting their practicality. However, the advent of wearable EEG technology provides the possibility of mental workload assessment for real-life applications. Yet, a few studies that considered consumer-oriented EEG headsets for mental workload assessment only used a single database for validating the proposed methods, overlooking the potential for portability. In this research, we studied 60 recordings of participants playing a three-level n-back game, utilizing data from two EEG devices, Enobio and Muse, with distinctive characteristics such as sampling rate and channel configuration. Following the denoising of the EEG signals, we segmented the signals and applied the discrete wavelet transform (DWT) to decompose them into subbands. Then, we extracted Shannon entropy (SE) and wavelet log energy (WLE) features from all subbands. Subsequently, we fed the extracted features into five classifiers: support vector machine (SVM), k-nearest neighbors (kNNs), multilayer perceptron (MLP), AdaBoost, and the transformer network (TN). In comparing the results across all classifiers, the TN demonstrated superiority by achieving highest mean accuracy for Database M (88%) and Database E (85%). Given the consistent outcomes achieved with the TN classifier across both databases and utilizing a three-level n-back game, our findings indicate that the proposed method holds promise for real-life applications.