Brain–Computer Interface Technologies in the Coming Decades

Abstract

As the proliferation of technology dramatically infiltrates all aspects of modern life, in many ways the world is becoming 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.

On robust spatial filtering of EEG in nonstationary environments

“…This change in paradigm from "let the user learn" to "let the machine learn" largely reduced training times and significantly increased the attraction of these systems (Blankertz et al, 2006a). Although technology has rapidly advanced during the last decade, e.g., dry electrode EEG recordings (Popescu et al, 2007), zero training systems Fazli et al, 2009) and robust machine learning methods (Lotte and Guan, 2011;, many challenges limiting a large scale application of BCIs in clinical practice and its usage as assistive technology for disabled people still exists (Dietrich et al, 2010;Krusienski et al, 2011;Lance et al, 2012). Several pilot studies Lim et al, 2012) have demonstrated the utility of BCI for medical application, but much more research is needed in this direction.…”

An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

“…It has shown potential applications in many fields: rehabilitation [1], [2], games [4], and military [5]. Event-related potential (ERP) is a type of brain signal defined as an electrical response of the cortex to stimuli.…”

Design and Implementation of a Speller based on EMG Signal

“…BCI allow users to communicate directly with their surroundings making use of their brain signals with the aid of systems like BCI Speller [1], [2]. Researchers have been using physiological data such as Electroencephalography (EEG), Electrooculography (EOG), Electromyography (EMG), Eelectrocorticography (ECoG), Electrodermal Activity (EDA), Blood Oxygen Saturation, Respiration, Skin Temperature etc.…”