Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multimodal Emotion Recognition Based on Facial Expressions, Speech, and EEG.

IEEE open journal of engineering in medicine and biology·2024

Same author

A Reparameterization Multifeature Fusion CNN for Arrhythmia Heartbeats Classification.

Computational and mathematical methods in medicine·2022

Same author

Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals.

Journal of healthcare engineering·2022

Same author

Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition.

Computational intelligence and neuroscience·2022

Same author

A Human-Machine Interface Based on an EOG and a Gyroscope for Humanoid Robot Control and Its Application to Home Services.

Journal of healthcare engineering·2022

Same author

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis.

Brain sciences·2021

Search research articles

Related Experiment Video

Updated: Nov 15, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning.

Man Li^1,2, Feng Li^1,2, Jiahui Pan^3,4

¹School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.

Sensors (Basel, Switzerland)

|March 6, 2021

Summary

This study introduces MindGomoku, a novel brain-computer interface (BCI) game using electroencephalogram (EEG) signals for accessible entertainment. The system achieves high accuracy and stability, offering new gaming possibilities for everyone, including the disabled.

Keywords:

BCI game Bayesian deep learning P300 brain–computer interface (BCI)electroencephalogram (EEG)

More Related Videos

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Related Experiment Videos

Last Updated: Nov 15, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

Published on: December 16, 2010

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Area of Science:

Neuroscience
Human-Computer Interaction
Game Design

Background:

Brain-computer interface (BCI) technology offers potential beyond assistive devices, extending into entertainment.
Current BCI games face limitations in control performance and user fatigue, hindering widespread adoption.
Developing practical and engaging BCI gaming experiences requires integrating game design with BCI system capabilities.

Purpose of the Study:

To propose and evaluate MindGomoku, a P300 BCI game designed for natural and feasible gameplay using electroencephalogram (EEG) signals.
To demonstrate a novel approach by integrating game rules with BCI system design for enhanced user experience.
To introduce a simplified Bayesian convolutional neural network (SBCNN) algorithm for accurate BCI control with limited training data.

Main Methods:

Development of the MindGomoku game, incorporating P300 BCI paradigms tailored to game mechanics.
Implementation of a simplified Bayesian convolutional neural network (SBCNN) algorithm for processing EEG signals.
Conducting online control experiments with 10 subjects to assess system reliability and performance.

Main Results:

All 10 subjects successfully controlled the MindGomoku game.
The system achieved an average control accuracy of 90.7%.
Subjects played the game for an average of over 11 minutes, demonstrating sustained engagement.

Conclusions:

The proposed MindGomoku system demonstrates stability and effectiveness for BCI-based gaming.
The SBCNN algorithm enables high accuracy with limited EEG training samples.
This research expands the potential of BCI technology for entertainment and accessibility, particularly for individuals with disabilities.