Header image 1Header image 2Header image 3Header image 4Header image 5

Brain Signals Laboratory

EEG, neuropsychiatry, diagnosis...

Tyler Grummett and Azinsadat Janani (PhD students)
illustrating what they ask of their research volunteers

 

 

Research Summary

The Brain Signals Laboratory evolved out of the EEG Research Laboratory. Initially motivated by novel EEG findings from basic animal research in the Epilepsy Laboratory, the EEG Research Laboratory was established with funding from The Wellcome Trust, UK, in 2003. It is a high-level neurophysiological research unit with a Faraday cage and a 128-channel, high sampling-rate, digital recording system. The initial aim of the research was to determine if patients with neuropsychiatric disorders exhibited disturbances of gamma EEG rhythms (30-100 Hz), with a special focus on epilepsy.

Since its establishment, other studies have been undertaken which have addressed; brain correlates of sensory processing, cognition, meditation and the functional significance of high-frequency brain rhythms. These and other projects are listed below. As a consequence of the diversity of experimentation now undertaken, we have renamed the laboratory. The equipment now includes a 256-channel EEG recording system as well as a 128-channel active (pre-amplified on the scalp) EEG recording system, plus a range of sensors for physiological measures (eg respiration, temperature, GSR).

Research Projects

It is known that electrical signals from muscle (EMG) contaminate the measurement of electrical signals from brain (EEG). We previously evaluated this issue in scalp signals from six awake volunteers both before and after curare-induced paralysis-with-ventilation. EMG contaminates EEG at frequencies from 20 Hz but most significantly at frequencies above 30 Hz and from electrodes situated around the skull base. Using this data, we were able to show the benefits of several statistical and mathematical processes in diminishing EMG contamination, namely, Independent Component Analysis and surface Laplacian transforms. We recently showed that when applying these processes sequentially, EMG contamination is largely eliminated: processed EEG can be used to examine EEG phenomena expressed in frequencies to 80 or 100 Hz, instead of less than the 20 Hz normally expected. We call our processed, EMG-minimised recordings “wideband EEG”.
PhD student Azinsadat Janani, is now systematically evaluating other published EMG contamination removal algorithms, such as canonical correlation analysis and wavelet analysis. Azin has found, recently, that Beamformer analysis can be used to identify extracranial sources of contamination of the EEG (which is mainly EMG), providing another EMG removal approach.

We have a data-bank of 632 patients with various disorders including 125 with epilepsy and 100 controls. The plan is to apply the combined processing methods (above) to our scalp recordings and then to search for improved biomarkers of CNS disorders. Since 2014, PhD student, Tyler Grummett, used this group in studies of advanced analytic methods. His suit of measures is adjacency matrices using coherence and transfer entropy between electrodes, and graph theory examining electrode connectivity more widely. Finally, Tyler is using automatic classifiers (Support Vector Machines and Artificial Neural Nets). The methods do have classification potential, possibly good enough to have clinical diagnostic use.

There are a variety of measures of connectivity between signals. PhD candidate Hanieh Bakhshayesh has compared these measures to determine their suitability for detecting functional and effective connectivity between brain signals. She has now studied in excess of 40 measures, both linear and non-linear, examining their sensitivity and specificity, performance on non-stationary signals, and performance with different noise powers on simulated signals where the “truth” is known. Current work is focussed on similar tests on EEG, where we do not have 100% knowledge of the relationships between signals.

With the new methods of EMG de-contamination of scalp recordings, Dr DeLosAngeles has reanalysed data from meditators entering progressively deeper meditative states and then progressively exiting. The findings are clear, progressively deeper states are associated with progressive changes in EEG consistent with increasing concentration. The findings have now been published in the International Journal of Psychophysiology.

It is thought that chronic contraction of cranial muscles leads to headaches. Quantitation of muscle contraction in different headache types (migraine, tension headache, cervicogenic headache) should lead to a better understanding of the pathophysiological of headache, and, possibly, permit better classification of headache patients. In other studies, we have developed reliable methods to identify and separate muscle signals from the mixture of brain and muscle signals measured at the scalp. Accordingly, accurate study of EMG in patients with headache is now possible and is less invasive, less time-consuming, and more comprehensive in measuring the activity of whole cranial muscles than in previous headache/muscle studies. Azin Janani, PhD student, will record muscle activity in different headache groups during the well period between headaches, as well as during the acute headache phase, comparing the muscle activity in these conditions and between individuals with headache disorder and those without. Using tabulated clinical information on headache sufferers and controls, prepared by medial student Nicole Fenton, Azin has recently determined that individuals with migraine appear to have central frontal (frown/surprise) muscle activity in excess of controls, possibly implicating frontalis muscle contraction in migraine physiology.

Selected Publications

Fitzgibbon SP, DeLosAngeles D, Lewis TW, Powers DM, Grummett TS, Whitham EM, Ward LM, Willoughby JO, Pope KJ (2016) Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis. Clinical  Neurophysiology, 127(3):1781-93

 

DeLosAngeles D, Williams G, Burston J, Fitzgibbon SP, Lewis TW, Grummett TS,
Clark CR, Pope KJ, Willoughby JO (2016) Electroencephalographic correlates of states of

concentrative meditation. International Journal of Psychophysiology, 110:27-39

 

Grummett TS, Leibbrandt RE, Lewis TW, DeLosAngeles D, Powers DM, Willoughby JO, Pope KJ, Fitzgibbon SP (2015) Measurement of neural signals from inexpensive, wireless and dry EEG systems. Physiological Measurement, 36(7):1469-84

 

Fitzgibbon SP, DeLosAngeles D, Lewis TW, Powers DM, Whitham EM, Willoughby JO, Pope KJ (2015) Surface Laplacian of scalp electrical signals and independent component analysis resolve EMG contamination of electroencephalogram. International Journal of Psychophysiology, 97(3):277-84

 

Broberg IM, Pope K, Olsson T, Shuttleworth CW, and Willoughby JO (2014) Spreading depression: Evidence of five electroencephalogram phases. Journal of Neuroscience Research, 92(10):1384-1394

 

Grummett TS, Fitzgibbon SP, Lewis TW, DeLosAngeles D, Whitham EM, Pope K, Willoughby JO (2014) Constitutive spectral EEG peaks in the gamma range: suppressed by sleep, reduced by mental activity and resistant to sensory stimulation. Frontiers in Human Neuroscience, 8:Article 927

 

Fitzgibbon SP, Lewis TW, Powers DM, Whitham EW, Willoughby JO, Pope KJ (2013) Surface laplacian of central scalp electrical signals is insensitive to muscle contamination. IEEE Transactions on Biomedical Engineering, 60(1):4-9

 

Atyabi A, Luerssen M, Fitzgibbon S and Powers D (2012) The use of Evolutionary Algorithm-based methods in EEG based BCI systems. In Girolamo Fornarelli and Luciano Mescia, ed Swarm Intelligence for Electric and Electronic Engineering, Hershey, USA: IGI Global, pp326-344

 

 

Investigators

  • John O Willoughby, MBBS, PhD, FRACP

  • Kenneth J Pope, AMusA, BSc(Ma), BE(Hons), PhD(Cantab)

  • Trent W Lewis, BSc(Hons), PhD

  • Sherry Rhandawa, BEng, MEng, GradCertTertEd, PhD

  • Emma M Whitham, BSc, BMBCh(Oxon), PhD

  • Dylan DeLosAngeles, BSc(Hons), PhD

Students

  • Tyler Grummett, BBehavSci, BSc(Hons), PhD Student

  • Hanieh Bakhshayesh, BEng(Electr), BEng(Hons), PhD Student

  • Azinsadat Janani, BBioMedEng, MBioMedEng, PhD Student

  • Nicole Fenton, MD Advanced Studies Student


CNS page footer image
Website by Journeyman Systems