080122 Schalk

Dr. Gerwin Schalk

Wadsworth Center, Albany, USA

Brain-Computer Interfacing Using Electrocorticography (ECoG)

Brain-computer interfaces (BCIs) convert brain signals into outputs that communicate a user's intent. BCIs can be used by people who are severely paralyzed to communicate and interact with their environment. However, practical applications of BCI technology are currently impeded by the limitations and requirements of the prevailing non-invasive and invasive methods. Non-invasive BCIs use electroencephalographic activity (EEG) recorded from the scalp. While EEG-based BCIs support higher performance than often assumed, the acquisition of high levels of control typically requires extensive user training. Invasive BCIs use local activity from multiple neurons recorded within the brain. Signals recorded within cortex have higher fidelity and might support BCI systems that require less training than EEG-based systems. However, clinical implementations of intracortical BCIs are currently impeded mainly by the difficulties in maintaining stable long-term recordings. Electrocorticographic (ECoG) recordings from the cortical surface could be a powerful and practical alternative to current non-invasive and invasive BCI recording methods. ECoG has higher spatial resolution than EEG, broader bandwidth, higher characteristic amplitude, and far less vulnerability to artifacts such as EMG or ambient noise. At the same time, because ECoG does not require penetration of the cortex, it is likely to have greater long-term stability and to produce less tissue damage and reaction than intracortical recordings. This talk will review the current state of ECoG-based BCI systems. It will first describe the ECoG responses that correspond to actual or imagined limb movements. It will then describe the one- and two-dimensional BCI experiments supported by these tasks. Finally, it will show that it is possible to use ECoG to accurately decode specific parameters of hand movements, finger movements, and speech. In summary, these recent findings are further evidence that ECoG could be a robust and practical alternative for clinical application of BCI technology.