Machine learning classification of motivational states: Insights from EEG analysis of perception and imagery

The investigation of neural correlates of mental imagery and perception has been a pivotal area of research in cognitive neuroscience, offering significant insights into how the brain represents both imagined and perceived experiences. While previous studies have successfully identified electrophysiological markers associated with motor and perceptual imagery, the neural signatures of motivational imagery — encompassing desires, needs, and cravings — remain underexplored. This study employs different machine learning classifiers applied to EEG data to classify and compare neural representations of twelve distinct motivational states under perception and imagery conditions. We conducted experiments using 14-channel and 18-channel EEG configurations to capture and analyze the neural responses of participants exposed to various stimuli. Our primary aims were to evaluate classification performance, measured by accuracy, and assess the impact of electrode density on performance. The results indicate that perception conditions generally yield higher accuracy in distinguishing motivational states than imagery conditions. Specifically, primary needs and somatosensory states exhibited strong and clear neural patterns in perception, with a peak accuracy of 88% in the 18-channel setup, while the accuracy for imagined states was more variable. Comparisons between 14-channel and 18-channel configurations revealed that higher electrode density slightly improved performance but was not significantly superior.