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Front Neuroengineering. 2010; 3: 9.
Published online 2010 June 24. doi:  10.3389/fneng.2010.00009
PMCID: PMC2903308

Can Electrocorticography (ECoG) Support Robust and Powerful Brain–Computer Interfaces?

Brain–computer interfaces (BCIs) use brain signals to communicate a user's intent (Wolpaw et al., 2002). Because these systems directly translate brain activity into action, without depending on peripheral nerves and muscles, they can be used by people with severe motor disabilities. Successful translation of BCI technology from the many recent laboratory demonstrations into widespread and valuable clinical applications is currently substantially impeded by the problems of traditional non- invasive or intracortical signal acquisition technologies.

Non-invasive BCIs use electroencephalographic (EEG) activity recorded from the scalp (Birbaumer et al., 1999; Pfurtscheller et al., 2000; Wolpaw et al., 2002; Millan Jdel et al., 2004; Wolpaw and McFarland, 2004; Blankertz et al., 2007; McFarland et al., 2008). While non-invasive BCIs can support much higher performance than previously assumed (Wolpaw and McFarland, 2004; Müller and Blankertz, 2006; McFarland et al., 2008, 2010), such performance typically requires extensive user training and can also be variable. Intracortical BCIs use action potential firing rates or local field potential activity recorded from individual or small populations of neurons within the brain (Serruya et al., 2002; Taylor et al., 2002; Carmena et al., 2003; Shenoy et al., 2003; Santhanam et al., 2006; Donoghue et al., 2007; Velliste et al., 2008). Signals recorded within cortex may encode more information and might support BCI systems that require less training than EEG-based systems. However, clinical implementations are impeded mainly by the problems in achieving and maintaining stable long-term recordings from individual neurons and by the high variability in neuronal behavior (Shain et al., 2003; Donoghue et al., 2004). Despite encouraging evidence that BCI technologies can serve useful functions for severely disabled individuals (Kübler et al., 2005; Hochberg et al., 2006; Nijboer et al., 2008), these issues of non-invasive and action potential-based techniques in acquiring and maintaining robust recordings and BCI control remain crucial obstacles that currently impede widespread clinical use in humans.

In consequence, a critical challenge in designing BCI systems for widespread clinical application is the identification and optimization of a BCI method that combines good performance with robustness. In the current absence of robust techniques to extract high-fidelity signals from EEG or to record activity from within the brain over prolonged periods, the use of electrocorticographic (ECoG) activity recorded from the cortical surface could be a powerful and practical alternative. ECoG has higher spatial resolution than EEG (i.e., tenths of millimeters vs. centimeters, Freeman et al., 2000; Slutzky et al., 2010), broader bandwidth (i.e., 0–500 Hz, Staba et al., 2002, vs. 0–40 Hz), higher amplitude (i.e., 50–100 μV maximum vs. 10–20 μV), much greater signal-to-noise ratio (Ball et al., 2009), and far less vulnerability to artifacts such as EMG (Freeman et al., 2003). In addition to these superior general characteristics, a number of human studies (Schalk et al., 2007; Ball et al., 2008; Pistohl et al., 2008; Sanchez et al., 2008; Waldert et al., 2008; Gunduz et al., 2009; Kubanek et al., 2009) have recently shown that ECoG can provide information about movements that far exceeds that provided by EEG. Other studies (Leuthardt et al., 2004; Wilson et al., 2006; Schalk et al., 2008) demonstrated that this information in ECoG can be used to provide one- or two-dimensional BCI control with little training. In summary, these (predominantly human) studies have produced great excitement for ECoG recordings, because they demonstrate that ECoG can provide information about movements and other aspects of behavior that is in aspects relevant to BCI performance on par with, and can even exceed, the information provided by single-neuron recordings.

While these studies demonstrated ECoG's impressive capabilities, and while several other studies suggested that ECoG may have long-term robustness (Loeb et al., 1977; Bullara et al., 1979; Yuen et al., 1987; Pilcher and Rusyniak, 1993; Margalit et al., 2003), concrete quantitative evidence for ECoG's long-term stability has been missing. The recent study by Chao et al. (2010) provided this critical piece of information. This study evaluated ECoG-based decoding of hand position and arm joint angles during reaching movements. Data were recorded in two monkeys over a period of several months. This study confirmed and extended the previous finding that local field potentials recorded from the surface of the brain can be used to accurately decode different kinematic parameters of limb movements. More importantly, it also provided two other pieces of information. First, the authors showed that decoding performance does not significantly degrade with time, which suggests that the signal-to-noise ratio of ECoG recordings is robust over many months. Second, the authors also showed that there is no negative correlation between decoding performance and the time between model generation and model testing, which suggests that the neural representations that encode kinematic parameters of reaching movements are stable across the months of study.

In conclusion, the study by Chao and colleagues is of critical importance to the whole field of BCI research. It justifies previous excitement for ECoG recordings, and more forcefully suggests a realistic trajectory toward robust, powerful, and widespread clinical applications of BCI technology.


  • Ball T., Demandt E., Mutschler I., Neitzel E., Mehring C., Vogt K., Aertsen A., Schulze-Bonhage A. (2008). Movement related activity in the high gamma range of the human EEG. Neuroimage 41, 302–31010.1016/j.neuroimage.2008.02.032 [PubMed] [Cross Ref]
  • Ball T., Kern M., Mutschler I., Aertsen A., Schulze-Bonhage A. (2009). Signal quality of simultaneously recorded invasive and non-invasive EEG. Neuroimage 46, 708–71610.1016/j.neuroimage.2009.02.028 [PubMed] [Cross Ref]
  • Birbaumer N., Ghanayim N., Hinterberger T., Iversen I., Kotchoubey B., Kubler A., Perelmouter J., Taub E., Flor H. (1999). A spelling device for the paralysed. Nature 398, 297–29810.1038/18581 [PubMed] [Cross Ref]
  • Blankertz B., Dornhege G., Krauledat M., Muller K. R., Curio G. (2007). The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37, 539–55010.1016/j.neuroimage.2007.01.051 [PubMed] [Cross Ref]
  • Bullara L. A., Agnew W. F., Yuen T. G., Jacques S., Pudenz R. H. (1979). Evaluation of electrode array material for neural prostheses. Neurosurgery 5, 681–68610.1227/00006123-197912000-00006 [PubMed] [Cross Ref]
  • Carmena J. M., Lebedev M. A., Crist R. A., O'Doherty J. A., Santucci D. M., Dimitrov D. F., Patil P. G., Henriquez C. S., Nicolelis M. A. L. (2003). Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, 1–1610.3389/fneng.2010.00003 [PMC free article] [PubMed] [Cross Ref]
  • Chao Z. C., Nagasaka Y., Fujii N. (2010). Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys. Front. Neuroengineering 3:310.3389/fneng.2010.00003 [PMC free article] [PubMed] [Cross Ref]
  • Donoghue J. P., Nurmikko A., Black M., Hochberg L. R. (2007). Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol. (Lond.) 579, 603–61110.1113/jphysiol.2006.127209 [PubMed] [Cross Ref]
  • Donoghue J. P., Nurmikko A., Friehs G., Black M. (2004). Development of neuromotor prostheses for humans. Suppl. Clin. Neurophysiol. 57, 592–60610.1016/S1567-424X(09)70399-X [PubMed] [Cross Ref]
  • Freeman W. J., Holmes M. D., Burke B. C., Vanhatalo S. (2003). Spatial spectra of scalp EEG and EMG from awake humans. Clin. Neurophysiol. 114, 1053–106810.1016/S1388-2457(03)00045-2 [PubMed] [Cross Ref]
  • Freeman W. J., Rogers L. J., Holmes M. D., Silbergeld D. L. (2000). Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands. J. Neurosci. Methods 95, 111–12110.1016/S0165-0270(99)00160-0 [PubMed] [Cross Ref]
  • Gunduz A., Sanchez J. C., Carney P. R., Principe J. C. (2009). Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans motor control features. Neural. Netw. 22, 1257–127010.1016/j.neunet.2009.06.036 [PubMed] [Cross Ref]
  • Hochberg L. R., Serruya M. D., Friehs G. M., Mukand J. A., Saleh M., Caplan A. H., Branner A., Chen D., Penn R. D., Donoghue J. P. (2006). Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–17110.1038/nature04970 [PubMed] [Cross Ref]
  • Kubanek J., Miller K. J., Ojemann J. G., Wolpaw J. R., Schalk G. (2009). Decoding flexion of individual fingers using electrocorticographic signals in humans. J. Neural Eng. 6, 66001.10.1088/1741-2560/6/6/066001 [PMC free article] [PubMed] [Cross Ref]
  • Kübler A., Nijboer F., Mellinger J., Vaughan T. M., Pawelzik H., Schalk G., McFarland D. J., Birbaumer N., Wolpaw J. R. (2005). Patients with ALS can use sensorimotor rhythms to operate a brain–computer interface. Neurology 64, 1775–177710.1212/01.WNL.0000158616.43002.6D [PubMed] [Cross Ref]
  • Leuthardt E. C., Schalk G., Wolpaw J. R., Ojemann J. G., Moran D. W. (2004). A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 63–7110.1088/1741-2560/1/2/001 [PubMed] [Cross Ref]
  • Loeb G. E., Walker A. E., Uematsu S., Konigsmark B. W. (1977). Histological reaction to various conductive and dielectric films chronically implanted in the subdural space. J. Biomed. Mater. Res. 11, 195–21010.1002/jbm.820110206 [PubMed] [Cross Ref]
  • Margalit E., Weiland J., Clatterbuck R., Fujii G., Maia M., Tameesh M., Torres G., D'Anna S., Desai S., Piyathaisere D., Olivi A., de Juan E. J., Humayun M. (2003). Visual and electrical evoked response recorded from subdural electrodes implanted above the visual cortex in normal dogs under two methods of anesthesia. J. Neurosci. Methods 123, 129–13710.1016/S0165-0270(02)00345-X [PubMed] [Cross Ref]
  • McFarland D. J., Krusienski D. J., Sarnacki W. A., Wolpaw J. R. (2008). Emulation of computer mouse control with a noninvasive brain–computer interface. J. Neural Eng. 5, 101–11010.1088/1741-2560/5/2/001 [PMC free article] [PubMed] [Cross Ref]
  • McFarland D. J., Sarnacki W. A., Wolpaw J. R. (2010). Electroencephalographic (EEG) control of three- dimensional movement. J. Neural Eng. 7, 036007.10.1088/1741-2560/7/3/036007 [PMC free article] [PubMed] [Cross Ref]
  • Millan J. R., Renkens F., Mourino J., Gerstner W. (2004). Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans. Biomed. Eng. 51, 1026–103310.1109/TBME.2004.827086 [PubMed] [Cross Ref]
  • Müller K. R., Blankertz B. (2006). Toward noninvasive brain–computer interfaces. IEEE Signal Process. Mag. 23, 126–128
  • Nijboer F., Sellers E. W., Mellinger J., Jordan M. A., Matuz T., Furdea A., Halder S., Mochty U., Krusienski D. J., Vaughan T. M., Wolpaw J. R., Birbaumer N., Kubler A. (2008). A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin. Neurophysiol. 119, 1909–191610.1016/j.clinph.2008.03.034 [PMC free article] [PubMed] [Cross Ref]
  • Pfurtscheller G., Guger C., Muller G., Krausz G., Neuper C. (2000). Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211–21410.1016/S0304-3940(00)01471-3 [PubMed] [Cross Ref]
  • Pilcher W., Rusyniak W. (1993). Complications of epilepsy surgery. Neurosurg. Clin. N. Am. 4, 311–325 [PubMed]
  • Pistohl T., Ball T., Schulze-Bonhage A., Aertsen A., Mehring C. (2008). Prediction of arm movement trajectories from ECoG-recordings in humans. J. Neurosci. Methods 167, 105–11410.1016/j.jneumeth.2007.10.001 [PubMed] [Cross Ref]
  • Sanchez J. C., Gunduz A., Carney P. R., Principe J. C. (2008). Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics. J. Neurosci. Methods 167, 63–8110.1016/j.jneumeth.2007.04.019 [PubMed] [Cross Ref]
  • Santhanam G., Ryu S. I., Yu B. M., Afshar A., Shenoy K. V. (2006). A high-performance brain–computer interface. Nature 442, 195–19810.1038/nature04968 [PubMed] [Cross Ref]
  • Schalk G., Kubanek J., Miller K. J., Anderson N. R., Leuthardt E. C., Ojemann J. G., Limbrick D., Moran D., Gerhardt L. A., Wolpaw J. R. (2007). Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J. Neural Eng. 4, 264–27510.1088/1741-2560/4/3/012 [PubMed] [Cross Ref]
  • Schalk G., Miller K. J., Anderson N. R., Wilson J. A., Smyth M. D., Ojemann J. G., Moran D. W., Wolpaw J. R., Leuthardt E. C. (2008). Two-dimensional movement control using electrocorticographic signals in humans. J. Neural Eng. 5, 75–8410.1088/1741-2560/5/1/008 [PMC free article] [PubMed] [Cross Ref]
  • Serruya M. D., Hatsopoulos N. G., Paninski L., Fellows M. R., Donoghue J. P. (2002). Instant neural control of a movement signal. Nature 416, 141–14210.1038/416141a [PubMed] [Cross Ref]
  • Shain W., Spataro L., Dilgen J., Haverstick K., Isaacson M., Saltzman M., Turner J. N. (2003). Controlling cellular reactive responses around neural prosthetic devices using peripheral and local intervention strategies. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 186–18810.1109/TNSRE.2003.814800 [PubMed] [Cross Ref]
  • Shenoy K. V., Meeker D., Cao S., Kureshi S. A., Pesaran B., Buneo C. A., Batista A. P., Mitra P. P., Burdick J. W., Andersen R. A. (2003). Neural prosthetic control signals from plan activity. Neuroreport 14, 591–59610.1097/00001756-200303240-00013 [PubMed] [Cross Ref]
  • Slutzky M. W., Jordan L. R., Krieg T., Chen M., Mogul D. J., Miller L. E. (2010). Optimal spacing of surface electrode arrays for brain-machine interface applications. J. Neural Eng. 7, 26004.10.1088/1741-2560/7/2/026004 [PMC free article] [PubMed] [Cross Ref]
  • Staba R. J., Wilson C. L., Bragin A., Fried I., Engel J. (2002). Quantitative analysis of high-frequency oscillations (80–500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J. Neurophysiol. 88, 1743–1752 [PubMed]
  • Taylor D. M., Tillery S. I., Schwartz A. B. (2002). Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–183210.1126/science.1070291 [PubMed] [Cross Ref]
  • Velliste M., Perel S., Spalding M. C., Whitford A. S., Schwartz A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–110110.1038/nature06996 [PubMed] [Cross Ref]
  • Waldert S., Preissl H., Demandt E., Braun C., Birbaumer N., Aertsen A., Mehring C. (2008). Hand movement direction decoded from MEG and EEG. J. Neurosci. 28, 1000–100810.1523/JNEUROSCI.5171-07.2008 [PubMed] [Cross Ref]
  • Wilson J. A., Felton E. A., Garell P. C., Schalk G., Williams J. C. (2006). ECoG factors underlying multimodal control of a brain–computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 246–25010.1109/TNSRE.2006.875570 [PubMed] [Cross Ref]
  • Wolpaw J. R., Birbaumer N., McFarland D. J., Pfurtscheller G., Vaughan T. M. (2002). Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113, 767–79110.1016/S1388-2457(02)00057-3 [PubMed] [Cross Ref]
  • Wolpaw J. R., McFarland D. J. (2004). Control of a two-dimensional movement signal by a noninvasive brain–computer interface in humans. Proc. Natl. Acad. Sci. U.S.A. 101, 17849–1785410.1073/pnas.0403504101 [PubMed] [Cross Ref]
  • Yuen T. G., Agnew W. F., Bullara L. A. (1987). Tissue response to potential neuroprosthetic materials implanted subdurally. Biomaterials 8, 138–14110.1016/0142-9612(87)90103-7 [PubMed] [Cross Ref]

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