The effects of blood vessels on electrocorticography

Electrocorticography, primarily used in a clinical context, is becoming increasingly important for fundamental neuroscientific research, as well as for brain-computer interfaces. Recordings from these implanted electrodes have a number of advantages over non-invasive recordings in terms of band width, spatial resolution, smaller vulnerability to artifacts and overall signal quality. However, an unresolved issue is that signals vary greatly across electrodes. Here, we examine the effect of blood vessels lying between an electrode and the cortex on signals recorded from subdural grid electrodes. Blood vessels of different sizes cover extensive parts of the cortex causing variations in the electrode-cortex connection across grids. The power spectral density of electrodes located on the cortex and electrodes located on blood vessels obtained from eight epilepsy patients is compared. We find that blood vessels affect the power spectral density of the recorded signal in a frequency-band-specific way, in that frequencies between 30 and 70 Hz are attenuated the most. Here, the signal is attenuated on average by 30-40% compared to electrodes directly on the cortex. For lower frequencies this attenuation effect is less pronounced. We conclude that blood vessels influence the signal properties in a non-uniform manner.

[1]  Robert Oostenveld,et al.  Gain of the human dura in vivo and its effects on invasive brain signal feature detection , 2010, Journal of Neuroscience Methods.

[2]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[3]  Pongkiat Kankirawatana,et al.  Advances in intracranial monitoring. , 2008, Neurosurgical focus.

[4]  Nick F. Ramsey,et al.  Automated electrocorticographic electrode localization on individually rendered brain surfaces , 2010, Journal of Neuroscience Methods.

[5]  J. A. Wilson,et al.  Two-dimensional movement control using electrocorticographic signals in humans , 2008, Journal of neural engineering.

[6]  Andreas Schulze-Bonhage,et al.  Signal quality of simultaneously recorded invasive and non-invasive EEG , 2009, NeuroImage.

[7]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[8]  Gerwin Schalk,et al.  Brain–computer interfacing based on cognitive control , 2010, Annals of neurology.

[9]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[10]  C. Gabriel,et al.  Electrical conductivity of tissue at frequencies below 1 MHz , 2009, Physics in medicine and biology.

[11]  Eric Halgren,et al.  Sequential Processing of Lexical, Grammatical, and Phonological Information Within Broca’s Area , 2009, Science.

[12]  Horst Bischof,et al.  A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans , 2009, Epilepsy & Behavior.

[13]  N. Crone,et al.  High-frequency gamma oscillations and human brain mapping with electrocorticography. , 2006, Progress in brain research.

[14]  R. Lesser,et al.  Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band. , 1998, Brain : a journal of neurology.

[15]  G. Schalk,et al.  ECoG factors underlying multimodal control of a brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.