Single trial detection of error-related potentials in brain-machine interfaces: a survey and comparison of methods.

Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use.

A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders.

Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions.

Emotional State Estimation using Sensor Fusion of EEG and EDA.

Emotions potentially have a significant impact on human actions and recognizing affective states is an effective way of implementing Brain-Computer Interface (BCI) systems which process brain signals to allow direct communication and interaction with the environment. In this paper, a real-time emotion recognition model was developed on the basis of physiological signals. A sensor fusion method is developed to detect human emotion by using data acquired from ElectroEncephaloGraphy (EEG) and ElectroDermal Activity (EDA) sensors.