Assessing the impact of stimulation environment and error probability on ErrP EEG response, detection and subject attention: an explorative study

Objective This study aims to investigate the impact of stimulus environments (Virtual Theatre vs Monitor) and error probabilities ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mn>20</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> vs <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:mn>50</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> ) on attentional states, Error Potentials (ErrP), and machine learning classification performance. Approach EEG signals were recorded using different protocols, and features were extracted for subsequent analysis from single-trial response and attention level was computed from the second preceding error processing stimulation. Results The results indicate significant differences across conditions: the Monitor environment consistently elicited higher and faster ErrP responses and elevated attentional states compared to Virtual Theatre. Additionally, classification performance in the Monitor environment outperformed Virtual Theatre consistently. Further analysis revealed that the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:mn>20</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> error probability protocol yielded increased ErrP responses, heightened attentional states, and superior classification performance compared to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:mn>50</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> protocol. Classification performance under the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:mrow><mml:mn>20</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> error probability condition consistently exceeded <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:mrow><mml:mn>75</mml:mn><mml:mi>%</mml:mi></mml:mrow></mml:math> validation and test sets. Moreover, a significant correlation between attention-related features and ErrP characteristics was observed, highlighting the intricate relationship between error processing and attentional engagement. Relevance These findings underscore the importance of considering stimulus environments and error probabilities in cognitive neuroscience research and machine learning applications. Understanding these factors can inform experimental design and model development, ultimately advancing our comprehension of cognitive processes and enhancing real-world applications of machine learning algorithms.