Abstract
Brain-computer interface (BCI) is relatively a new approach to communication between man and machine, which translates brain activity into commands for communication and control. As BCI is capable of detecting human intentions, it is a promising communication tool for paralyzed patients for communicating with external world. Many of the current BCI systems employ electroencephalogram (EEG) which is the most widely used noninvasive brain activity recording technique. EEG signal carries potential features to identify and decode human intentions and mental tasks. Recently, many researchers have started exploiting the possibilities of BCI in entertainment and cognitive skill enhancement. BCI-based games have been identified as a unique entertainment mechanism nowadays, “controlling a 2-D, 3-D or virtual computer game solely by player’s brain waves.” BCI games work based on a neurofeedback paradigmwhich allows an individual to self-regulate his brain signal in response to the real-time visual or auditory feedback of his brain waves/features. This neurofeedback in a gaming environment motivates and trains the players to control his brain features toward the desired stage (self-regulation). This chapter explores the state-of-the-art BCI technology in neurofeedback games, employing EEG signal. It also provides a survey of the existing EEG-based neurofeedback games and evaluates their success rates, challenging factors and influence on players. In neurofeedback games, a number of features extracted from EEG accompanied with sustained attention, selective attention, visuospatial attention, motor imagery, eye movements, etc. have been employed as distinct control signals.We will briefly review and compare various signal processing methodologies and machine-learning techniques employed in those studies to extract and decode the brain features. Besides the structure and algorithms used in neurofeedback games, the therapeutic effects of neurofeedback training and its capabilities for the enhancement of cognitive skills will also be briefly discussed in this chapter. Neurofeedback training helps to rewire brain’s underlying neural circuits and to improve brain functions. Therefore, it is considered as an effective tool for boosting cognitive skills of both healthy and the disabled. Specifically, neurofeedback has been considered as an efficient treatment modality for individuals with attention-deficit hyperactive disorder (ADHD). ADHD is characterized by three behavioral symptoms: inattention, hyperactivity and impulsivity. Along with the conventional intervention strategies such as medication, behavioral treatments, etc., neurofeedback in BCI games has also been emerging as a promising modality for treating the attention deficit. We will also discuss portable and economical EEG recording devices currently employed in BCI-based brain training modules/games. Finally, the chapter will be concluded with a brief overview of the neurofeedback developments in the context of BCI-based games until now, their potential impact on the healthy as well as on people with neurological disorders, challenges in transferring the successful protocols from laboratories into the market and hurdles in real-time BCI system design and development.
| Original language | English |
|---|---|
| Title of host publication | Signal Processing and Machine Learning for Brain-Machine Interfaces |
| Publisher | Institution of Engineering and Technology |
| Pages | 301-329 |
| Number of pages | 29 |
| ISBN (Electronic) | 9781785613982 |
| DOIs | |
| Publication status | Published - 1 Jan 2018 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© The Institution of Engineering and Technology 2018.
Keywords
- ADHD
- Attention-deficit hyperactive disorder
- BCI games
- BCI systems
- BCI technology
- BCI-based brain training modules
- Bioelectric signals
- Biology and medical computing
- Brain activity
- Brain functions
- Brain signal
- Brain-computer interfaces
- Cognitive skill enhancement
- Computer games
- Digital signal processing
- EEG signal
- EEG-based brain-computer interface technology
- EEG-based neurofeedback games
- Electrical activity in neurophysiological processes
- Electrodiagnostics and other electrical measurement techniques
- Electroencephalogram
- Electroencephalography
- Feature extraction
- Feedback
- Gaming environment
- Knowledge engineering techniques
- Learning (artificial intelligence)
- Machine-learning techniques
- Medical disorders
- Medical signal processing
- Neurofeedback developments
- Neurofeedback paradigm
- Neurofeedback training
- Neurological disorders
- Neurophysiology
- Signal processing and detection
- Signal processing methodologies
- User interfaces
- Virtual computer game