Rhythm is an essential element of dancing and music. To investigate the neural mechanisms underlying how rhythm is learned, we recorded electroencephalographic (EEG) data during a rhythm-reproducing task that asked participants to memorize an auditory stimulus and reproduce it via tapping. Based on the behavioral results, we divided the participants into Learning and No-learning groups. EEG ana...

This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable b...

During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students' expressions to determine whet...

The ability to learn not only from experienced but also from merely fictive outcomes without direct rewarding or punishing consequences should improve learning and resulting value-guided choice. Using an instrumental learning task in combination with multiple single-trial regression of predictions derived from a computational reinforcement-learning model on human EEG, we found an early temporos...

The study of emotions in human-computer interaction has increased in recent years in an attempt to address new user needs. At the same time, it is possible to record brain activity in real-time and discover patterns to relate it to emotional states. This paper describes a machine learning approach to detect emotion from brain activity, recorded as electroencephalograph (EEG) with the Emotic Epo...

In this paper, we present an approach for graph signal representation of EEG toward deep learning-based modeling. In order to overcome the low dimensionality and spatial resolution of EEG, our approach divides the EEG signal into multiple frequency bands, builds an intra-band graph for each of them, and merges them with inter-band connectivity to obtain rich graph representation. The signal fea...

There exists a variety of electroencephalogram (EEG) based brain-computer interface (BCI) assisted stroke rehabilitation protocols which exploit the recognized nature of sensorimotor rhythms (SMRs) during motor movements. For novel approaches independent of motor execution, we investigate the changes in resting-state sensorimotor EEG with motor learning, resembling the process of post-stroke re...

Machine learning (ML) has been well recognized as an effective tool for researchers to handle the problems in signal and image processing.Machine learning is capable of offering automatic learning techniques to excerpt common patterns from empirical data and then make sophisticated decisions, based on the learned behaviors. Medicine has a large dimensionality of data and the medical application...

The application of transcranial slow oscillation stimulation (tSOS; 0.75 Hz) was previously shown to enhance widespread endogenous EEG slow oscillatory activity when applied during a sleep period characterized by emerging endogenous slow oscillatory activity. Processes of memory consolidation typically occurring during this state of sleep were also enhanced. Here, we show that the same tSOS app...

There is considerable evidence to support the hypothesis of relationship between paradoxical sleep (PS) and learning–memory processing. It has been suggested that PS is important in memory retention at the specific time course called PS windows (PSW). The time of PSWs occurrence and duration of these PSWs following the training sessions and, the neurochemical nature of PSWs has not been well kn...