Brain-computer interface (BCI) research is growing at a significant pace and, since the beginning of the 21st century, has seen explosive growth. The depth and breadth of BCI research in progress today is indicative of its application potential – this is exemplified by the year-on-year exponential increase in peer review journal publications, regular news items in the media, formation of BCI related companies and substantial investment in BCI-specific projects. BCI technology can provide a communication pathway from the brain to the computer which does not rely on neuromuscular control therefore there are many potential beneficiaries of the technology. Even though BCI technology has been under investigation concertedly for the past ten years, there remain many challenges and barriers to providing this technology easily and effectively to the intended beneficiaries i.e., those who require an alternative means of communication/control such as people with neuromuscular deficiencies due to disease, spinal chord injury or brain damage. Being able to offer these people an alternative means of communication through BCI could have an obvious impact on their quality of life. There are other applications of BCI, yet to be fully proven and exploited, such as neurofeedback for stroke rehabilitation and epileptic seizure prediction, awareness/alertness detection for long distance drivers and personalised computing environment adaptation through workload monitoring. BCI is also emerging as an augmentative technology in computer games and virtual reality technology‎ and has been associated with numerous military applications.

As yet BCI is a nascent technology. There have been many advances but there are still a significant number of problems and issues to be resolved. The main problem stems from the fact the brain is a complex and non-stationary system and therefore neural signals, especially the electroencephalogram (EEG), are complex and unwieldy. In general, interpreting and discriminating biosignals is challenging. Traditional signal processing techniques have been applied in BCI and for biological signal processing however most biosystems are several orders of magnitude more complex than the man-made systems for which these techniques were developed. The complexity in biosystems arises from multiple factors such as multiple layered architectures, systematic changes due to evolution and mutation, the asynchronous nature of many interactions and the level of parallelism and redundancy, resulting in complex, random, or unpredictable behaviour. Biosignal processing methods require additional considerations to account for these inherent complexities.

Research in artificial intelligence (AI) and computational intelligence (CI) is producing novel methodologies to tackle the complexity of biosignals and this field is offering new insights into the way these signals can be analyzed and utilized. The AI and CI research community are well placed to tackle all of these challenges in BCI development and likewise BCI is an ideal domain for the study and application of AI and CI.