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Roy A. E. Bakay

✓Most patients who are candidates for brain–computer interface studies have an injury to their central nervous system and therefore may not be ideal for rigorous testing of the full abilities and limits of the interface. This is a report on a quadriplegic patient who appeared to be a reasonable candidate for intracranial implantation of neurotrophic electrodes. He had significant cortical atrophy in both the motor and parietal cortical areas but was able to generate signal changes on functional magnetic resonance images by thinking about hand movements. Only a few low-amplitude action potentials were obtained, however, and he was unable to achieve single-unit control. Despite this failure, the use of field potentials offered an alternative method of control and allowed him some limited computer interactions. There are clearly limits to what can be achieved with brain–computer interfaces, and the presence of cortical atrophy should serve as a warning for future investigators that less invasive techniques may be a more prudent approach for this type of patient.

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Elizabeth A. Felton, J. Adam Wilson, Justin C. Williams and P. Charles Garell

JM , Lebedev MA , Crist RE , O'Doherty JE , Santucci DM , Dimitrov DF , : Learning to control a brain-machine interface for reaching and grasping by primates . PLoS Biol 1 : E42 , 2003 2 Felton EA , Wilson JA , Radwin RG , Williams JC , Garell PC : Electrocorticogram-controlled brain-computer interfaces in patients with temporary subdural electrode implants . Neurosurgery 57 : 425 , 2005 3 Huggins JE , Levine SP , Fessler JA , Sowers WM , Pfurtscheller G , Graimann B , : Electrocorticogram as the basis

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Eric C. Leuthardt, Gerwin Schalk, Jarod Roland, Adam Rouse and Daniel W. Moran

T he notion that the brain can be directly accessed to allow a human being to control an external device with his or her thoughts alone is emerging as a real option in patients with motor disabilities. This area of study, known as neuroprosthetics, has sought to create devices, known as “brain-computer interfaces” (BCIs), that acquire brain signals and translate them into machine commands such that they reflect the intentions of the user. In the past 20 years, the field has rapidly progressed from fundamental neuroscientific discovery to initial

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Ivan S. Kotchetkov, Brian Y. Hwang, Geoffrey Appelboom, Christopher P. Kellner and E. Sander Connolly Jr.

B rain - computer interfaces, also called brain-machine interfaces or neural interface systems, represent a direct communication pathway between the brain and an external device. 18 , 32 , 59 The devices used for the BCI acquire brain signals such as an EEG rhythm or electrophysiological recordings of neuronal firing and translate them into commands intended by the user. Brain-computer interfaces accomplish this through novel output pathways that do not use the normal conduits of the nervous system. 32 During the past 40 years, BCIs have rapidly

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Tim Blakely, Kai J. Miller, Stavros P. Zanos, Rajesh P. N. Rao and Jeffrey G. Ojemann

H uman neocortical activity is dependent upon a wide variety of interdependent parameters. Across different spatial and temporal scales, this has been associated with a nonstationary neural signature in experimental recording. Attempts to capture and translate this neural activity as a control signal in a BCI have, to date, all relied upon dynamic decoding algorithms that either adapt continuously or are recalibrated between experimental runs. Brain-computer interfaces translate cortical signals for device control, bypassing the peripheral nervous system

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Eric C. Leuthardt, Zac Freudenberg, David Bundy and Jarod Roland

– 85 , 2006 13 Kostov A , Polak M : Parallel man-machine training in development of EEG-based cursor control . IEEE Trans Rehabil Eng 8 : 203 – 205 , 2000 14 Kubler A , Nijboer F , Mellinger J , Vaughan TM , Pawelzik H , Schalk G , : Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface . Neurology 64 : 1775 – 1777 , 2005 15 Leuthardt EC , Miller KJ , Schalk G , Rao RP , Ojemann JG : Electrocorticography-based brain computer interface–the Seattle experience . IEEE Trans Neural Syst

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Reinhold Scherer, Stavros P. Zanos, Kai J. Miller, Rajesh P. N. Rao and Jeffrey G. Ojemann

T he 2 dominant paradigms for brain-computer interfacing today rely on noninvasive recording from the scalp (known as electroencephalography or EEG) 3 , 38 and invasive techniques based on intracortical implants that are placed inside the brain. 7 , 15 Although EEG-based systems are cheap and relatively easy to build, the EEG signals themselves are extremely noisy, thereby limiting the bandwidth of control signals that can be reliably extracted. On the other hand, the signals recorded using intracortical implants are much stronger, typically allowing one

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Pantaleo Romanelli, Marco Piangerelli, David Ratel, Christophe Gaude, Thomas Costecalde, Cosimo Puttilli, Mauro Picciafuoco, Alim Benabid and Napoleon Torres

emerging tool for both brain mapping 10 , 14 , 15 and BCI, 5 , 18 due to its high signal-to-noise ratio. It allows the examination of high-frequency bands (unavailable for scalp electroencephalography [EEG] recordings) and allows the use of spectral analysis ECoG recording from sensorimotor cortex. 14 At the current state of art, this method provides the most effective tool for brain-computer interface (BCI) applications, i.e., to drive robotic prostheses and to enhance neurorehabilitation. 21 , 27 , 28 , 35 , 42 The available commercial ECoG systems require cables

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Daniel R. Kramer, Krista Lamorie-Foote, Michael Barbaro, Morgan B. Lee, Terrance Peng, Angad Gogia, George Nune, Charles Y. Liu, Spencer S. Kellis and Brian Lee

F or patients with loss of function due to stroke or paralysis, the restoration of somatosensation has implications both for motor restoration 17 , 24 through brain-computer interface (BCI) systems 3 , 4 , 26 , 28 and as an independent aid for monitoring injury, pressure, and internal organ states. Direct electrical stimulation of the primary somatosensory cortex (S1) is a promising technique for generating artificial somatosensation in humans, having yielded reliable and safe outcomes. 2 , 5 , 13 , 14 , 16 Given the success of initial studies, the next step

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Pantaleo Romanelli, Marco Piangerelli, David Ratel, Christophe Gaude, Thomas Costecalde, Cosimo Puttilli, Mauro Picciafuoco, Alim Benabid and Napoleon Torres

emerging tool for both brain mapping 10 , 14 , 15 and BCI, 5 , 18 due to its high signal-to-noise ratio. It allows the examination of high-frequency bands (unavailable for scalp electroencephalography [EEG] recordings) and allows the use of spectral analysis ECoG recording from sensorimotor cortex. 14 At the current state of art, this method provides the most effective tool for brain-computer interface (BCI) applications, i.e., to drive robotic prostheses and to enhance neurorehabilitation. 21 , 27 , 28 , 35 , 42 The available commercial ECoG systems require cables