Brain-Computer Interface Technologies in the Coming Decades

so dynamic and complex that technological capabilities are overwhelming human capabilities to optimally interact with and leverage those technologies. Fortunately, these technological advancements have also driven an explosion of neuroscience research over the past several decades, presenting engineers with a remarkable opportunity to design and develop flexible and adaptive brain-based neurotechnologies that integrate with and capitalize on human capabilities and limitations to improve human-system interactions. Major forerunners of this conception are brain-computer interfaces (BCIs), which to this point have been largely focused on improving the quality of life for particular clinical populations and include, for example, applications for advanced communications with paralyzed or ?locked-in? patients as well as the direct control of prostheses and wheelchairs. Near-term applications are envisioned that are primarily task-oriented and are targeted to avoid the most difficult obstacles to development. In the farther term, a holistic approach to BCIs will enable a broad range of task-oriented and opportunistic applications by leveraging pervasive technologies and advanced analytical approaches to sense and merge critical brain, behavioral, task, and environmental information. Communications and other applications that are envisioned to be broadly impacted by BCIs are highlighted; however, these represent just a small sample of the potential of these technologies.

The Promise of Incorporating the Brain

The foundation of the technological promise of BCI concepts lays in the notion that brain activity can provide unique insights into people and their behavior, and that these insights can be used to develop systems that can change how humans interact with the world. For example:

? As the nervous system underlies human behavior, the central element of the nervous
system, the brain, holds vastly more information than can be deciphered through behavior alone. The wealth of additional information gained through leveraging the neural signatures provides the potential to develop fundamentally different human-computer interaction capabilities than are seen with current technologies.

? The processes of the human brain are highly variable, both across people and within an individual across time, and this variability underlies the variability observed in human behavior. As such, understanding and leveraging this neural variability may be useful for tailoring adaptive technologies to the individual user and their current mental state.

? The human brain is highly adaptable in specific ways, which enables a wide variety of human capabilities such as learning, adjusting to new tasks and environments, and evenovercoming many types of trauma. Understand overcoming many types of trauma. Understanding how the human brain adapts and tracking neural adaptation online may be useful for leveraging this inherent human capability to develop novel approaches to training, education, and rehabilitation.

The Future of BCI Technologies

While there is incredible potential for the development of future BCI applications waiting to be unlocked in the hundreds of indices of neural behavior that have been identified by the neuroscience research community, current and likely near term BCIs remain ?task-oriented? (i.e., where the application is directly oriented towards the task the user is trying to accomplish) and include: a) BCIs that are the primary interface for the task the user is explicitly performing, such as using brain signals to control the movement of a prosthetic; and b) BCIs that directly support
the task the user is performing but are not the primary interface, such as a system that monitors the user?s brain signals in order to predict performance while driving and to mitigate periods of predicted poor performance. Developers have and will likely continue to find success with taskoriented BCIs, where the application itself is controlling the conditions under which the user performs, as opposed to attempting to find brain indices that generalize across any task that a user may be performing. This is because task-oriented BCIs will have access to more context for what the user is actually doing, and thus greater capability for interpreting the incoming neural signals.

Future task-oriented BCIs, based on advances in sensor technologies, analysis algorithms, artificial intelligence, multi-aspect sensing of the brain, behavior, and environment through pervasive technologies, and computing algorithms, will be capable of collecting and analyzing brain data for extended time periods and are expected to become prevalent in many aspects of daily life. If and when brain-sensing technologies are worn during portions of people?s daily lives, the possibility of using the BCI infrastructure for ?opportunistic? applications arises. That is, once users are regularly wearing brain sensors for specific purposes, opportunistic BCIs, which are BCI technologies that provide the user with a benefit, but do not directly support the task the user is performing, can be employed without additional overhead. Example opportunistic BCIs could be pervasive computing applications that adjust the user?s local environment (such as the color of lighting, music, or perhaps even odor, or suggestions for dietary, exercise, entertainment, or treatment options) to alter or enhance the user?s mood or mental state, or medical applications that periodically screen the user for indicators of neural diseases and pursue a variety of mitigations. Such mitigations may include: generating tasks for further analysis and screening (moving the BCI into the task-oriented domain), suggesting the user see a doctor for diagnosis, or suggesting preventative measures. However, due to the lack of constraints under which such applications have to function, opportunistic BCI development will likely advance through large-scale collection and analysis of data over extended periods of time, as well as the development of techniques for extensive individual customization to the user. While these issues will limit near-term development, over the longer-time frame, opportunistic BCIs may have life-saving ramifications in addition to the many other potential benefits to medical, education, work, and social applications.

Conclusions

The current explosion of neuroscience research and neurotechnologies provides the opportunity to provide computers predictive capabilities for the emotional and cognitive states and processes of the people using them, potentially revolutionizing not only interfaces, but the basic interactions people have with these systems. However, to reach their full potential, the development of BCI technologies over the coming decades will have to overcome a number of obstacles. For example, the amazing abilities of people to adapt to dynamic, complex tasks and environments present difficulties in interpreting an individual?s neural processes and behavior at any given time. These difficulties may arise due to the signal noise caused by environmental effects, overlapping neural processes arising from the performance of multiple concurrent tasks, and changes in neural signatures over the short and long term, in addition to the wide variation in neural signals across individuals.

In order to address these obstacles, near-term applications are likely to be task-oriented, focusing on applications where neural signals can provide information that is difficult or impossible to obtain through other measures, where perfect performance is not required for the application to successfully produce value, and that emphasize application-specific performance instead of attempting to detect abstract constructs (i.e., attempting to predict performance declines at specific tasks over time, instead of attempting to predict general fatigue). Near-term applications are also more likely to be successful if they focus on the individual user, through calibration or individual-based classification algorithms, instead of attempting to perform across broad groups or utilize normative populations.

In the far-term, we envision a more holistic approach to BCIs that merges critical brain, behavioral, task, and environmental information obtained with advanced pervasive, multi-aspect sensing technologies, sophisticated analytical approaches, and enabled by advances in computational infrastructure such as extensions of cloud technologies. Such an approach may also benefit from exploring synergies between the human and the computer as well as the largescale collection of data consisting of both brain function (e.g. EEG, fMRI) and brain structure (e.g. diffusion weighted imaging) at multiple scales, ranging from individual neurons up to maps of the entire brain. This data could provide a great deal of insight into how differences and changes in physical brain structure, both within and between individuals, cause changes in the functional brain data that can be detected in real time, thus providing much greater capabilities to individualized BCI technologies. The pervasive integration of neurotechnologies will also avail the development of a broad range of opportunistic BCI technologies in the far term, which have the potential to dramatically influence quality of life on a daily basis if scientists and developers can overcome the hurdles associated with detecting and interpreting neural signatures in relatively unconstrained settings.

In this paper, a number of potential BCI technologies focused on communication and other applications have been described; however, these represent just a small sample of the broad future potential of these technologies. We have also focused the discussion of applications on relatively foreseeable breakthroughs in sensor, analysis, and computational technologies; however, unforeseen breakthroughs, such as a novel wearable sensing technology that provides ultra-high resolution, real-time imaging of both the spatial and temporal activities of the brain, would open the door to vastly wider set of applications.

Source: http://nextbigfuture.com/2012/11/brain-computer-interface-technologies.html

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