EEG Signal Quality of a Subcutaneous Recording System Compared to Standard Surface Electrodes

Purpose. We provide a comprehensive verification of a new subcutaneous EEG recording device which promises robust and unobtrusive measurements over ultra-long time periods. The approach is evaluated against a state-of-the-art surface EEG electrode technology. Materials and Methods. An electrode powered by an inductive link was subcutaneously implanted on five subjects. Surface electrodes were placed at sites corresponding to the subcutaneous electrodes, and the EEG signals were evaluated with both quantitative (power spectral density and coherence analysis) and qualitative (blinded subjective scoring by neurophysiologists) analysis. Results. The power spectral density and coherence analysis were very similar during measurements of resting EEG. The scoring by neurophysiologists showed a higher EEG quality for the implanted system for different subject states (eyes open and eyes closed). This was most likely due to higher amplitude of the subcutaneous signals. During periods with artifacts, such as chewing, blinking, and eye movement, the two systems performed equally well. Conclusions. Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods. The signal might be superior to surface EEG in some aspects and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.

[1]  Jens Juul Holst,et al.  Automated detection of hypoglycemia-induced EEG changes recorded by subcutaneous electrodes in subjects with type 1 diabetes--the brain as a biosensor. , 2010, Diabetes research and clinical practice.

[2]  D. Hewson,et al.  Evolution in impedance at the electrode-skin interface of two types of surface EMG electrodes during long-term recordings. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[3]  Esther Rodriguez-Villegas,et al.  Wearable Electroencephalography , 2010, IEEE Engineering in Medicine and Biology Magazine.

[4]  H Thoma,et al.  EEG Topography during Insulin-Induced Hypoglycemia in Patients with Insulin-Dependent Diabetes mellitus , 1996, European Neurology.

[5]  Itsik Dvir,et al.  An automatic ambulatory device for detection of AASM defined arousals from sleep: the WP100. , 2003, Sleep medicine.

[6]  R. E. Madsen,et al.  Subdural to subgaleal EEG signal transmission: The role of distance, leakage and insulating affectors , 2013, Clinical Neurophysiology.

[7]  W. Marsden I and J , 2012 .

[8]  A. Johansson,et al.  Electro-mechanical stability of surface EMG sensors , 2007, Medical & Biological Engineering & Computing.

[9]  O. Arias-Carrión,et al.  EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation , 2010, Reviews in the neurosciences.

[10]  Line Sofie Remvig,et al.  Detection of hypoglycemia associated EEG changes during sleep in type 1 diabetes mellitus. , 2012, Diabetes research and clinical practice.

[11]  Hans Berger,et al.  Über das Elektrenkephalogramm des Menschen , 1933, Archiv für Psychiatrie und Nervenkrankheiten.

[12]  Chin-Teng Lin,et al.  Real-time assessment of vigilance level using an innovative Mindo4 wireless EEG system , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[13]  H. Berger Über das Elektrenkephalogramm des Menschen , 1938, Archiv für Psychiatrie und Nervenkrankheiten.

[14]  David M. Himes,et al.  Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study , 2013, The Lancet Neurology.

[15]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.