Topic: event-related potentials

Cognitive neuroscience studies routinely concentrate on calculating the correlation coefficient between trial-averaged Event-Related Potentials (ERPs) and behavioral performance such as response time and accuracy. However there are some disadvantages in this traditional approach: 1) ignoring the variance of EEG data across trials, 2) requiring a large number of participants to find robust inferences, and 3) a lack of formal cognitive models to explain cognition. In this work, we used the drift-diffusion model to decompose perceptual decision making to underlying latent variables to explain be

Humans, as a social species, have an increased ability to detect and perceive visual elements involved in social exchanges, such as faces and eyes. The gaze, in particular, conveys information crucial for social interactions and social cognition. Researchers have hypothesized that in order to engage in dynamic face-to-face communication in real time, our brains must quickly and automatically process the direction of another person's gaze. There is evidence that direct gaze improves face encoding and attention capture and that direct gaze is perceived and processed more quickly than averted gaz

Although electroencephalography (EEG) has been used in clinical and research studies for almost a century, recent technological advances have made the equipment and processing tools more accessible outside laboratory settings. These low-cost alternatives can achieve satisfactory results in experiments such as detecting event-related potentials and classifying cognitive states. In our research, we use low-cost single-channel EEG to classify brain activity during the presentation of images of opposite emotional valence from the OASIS database. Emotional classification has already been achieved u