Feb 172011

The BCI encompasses multiple types of neural technology united by a common purpose of assisting, augmenting, or repairing human cognitive or sensory-motor functions at the cortical level.  Taking its roots from the EEG interfaces in early 1970s, the BCI field has gradually grown to include other non-invasive techniques, such as the NIRS (near infrared-spectroscopy), fMRI (functional magnetic resonance imaging), and MEG (magnetoencephalogram). It has also evolved to include the invasive technologies, such as the electrocorticogram (ECoG) and penetrating cortical electrodes. In 2010, an international committee, headed by Dr. Gerwin Schalk of the Wadsworth Center (Albany, NY), critically evaluated the trends and developments in the BCIs by focusing on its novel applications and technological improvements. The  committee examined 57 submissions and selected the winner of 2010 BCI Research Award – a team led by Dr. Guan Cuntai from A*STAR, Singapore – for his work on motor-imagery based BCI coupled to a robotic arm and used for rehabilitation after stroke. The 2011 BCI Research Award will be awarded during the 5th International BCI Workshop on Sept. 22-24, 2011 in Graz, Austria. In the analysis of nominations for the 2010 award, the EEG is clearly the predominant technology accounting for 75% of nominations, while the fMRI and ECoG accounting for 3.5% each, NIRS accounting for 1.8%, and penetrating electrodes accounting for 0.9%. Current philosophy in the BCI development is dominated by four assumptions, stated in a recent article by Prof. Jon Wolpaw of the Wadsworth Center: (1) intended actions are fully represented in the cerebral cortex; (2) neuronal action potentials can provide the best picture of an intended action; (3) the best BCI is one that records action potentials and decodes them; and (4) ongoing mutual adaptation by the BCI user and the BCI system is not very important. According to Prof. Wolpaw, these assumptions are flawed. Indeed, much of the motor control occurs at the spinal cord, brainstem, and deep brain levels. Further complication for BCI is that the cortical involvement in the motor control is state-dependent and continually adapts to optimize the performance in different tasks. Present generation of BCI algorithms do not account for such state-dependent and performance-driven adaptations therefore their effectiveness quickly degrades over a period of several days. Yet another level of complexity for decoding of cortical signals stems from the profound slowly-developing plasticity in the motor cortex after the stroke or spinal cord injury. Fortunately, novel adaptive learning algorithms, like those in the IBM’s Jeopardy-winning computer Watson, continue to grow in sophistication and eventually should attain the adaptability needed for handling the challenges of BCI.

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