tim's journal

Introduction

Electroencephalography (EEG) is a well-established method for studying human brain activity. Based on voltage fluctuations across the scalp, it is a fundamental tool in disciplines such as neurology, psychiatry, psychology and pharmacology. Hans Berger in 1929, using string galvanometers, reported the first human EEG recordings and showed that changes in the fluctuations were related to changes in cognitive state [1]. Two years later, Berger replicated his results using electronic amplifiers and a special oscillograph from the Carl Zeiss Foundation [2]. Since then, EEG has been based on electrodes and electronic amplifiers which require scalp abrasion and use of conductive paste or gel [3].

While tolerable when the recordings are short and infrequent, abrasion and conductive paste become a hindrance when long or frequent recordings are desired. Over time, the conductive paste can dry, requiring reapplication, or skin can regrow, necessitating re-abrasion. Frequent abrasion can cause irritation or even infection. Finally, application and cleanup take time, adding to the cost of frequent sessions. Recently, two groups [4,5] have reported on EEG sensors requiring neither abrasion nor conductive paste. The suitability of such sensors remains controversial. In this dissertation, I propose and evaluate a number of tests to fully characterize such “dry” sensors.

Background
Liverpool surgeon Richard Caton discovered electrical activity in the brain in 1875. Using Lord Kelvin’s reflecting galvanometer, he probed the exposed cortex of rabbit and monkeys and reported suppressed fluctuations in response to the interruption of light incident on the animals’ eyes. His discovery was to sit quietly until 1890, when Polish psychologist Adolf Beck independently replicated Caton’s work. By 1912, using Willem Einthoven’s string galvanometer [6], Russian physiologist Pravdich-Neminski had produced a skull-intact photographic record of electrical activity in dogs which he called an electrocerebrogram. Berger, who had unsuccessfully initiated his research using a capillary electrometer , made the first skull-intact recordings from humans in 1924 using a modified string galvanometer [7]. For these recordings he coined the term elektroenkephalogramm from which we get our more modern electroencephalogram. Ten years later, Adrains and Matthews cemented Berger’s claims by duplicating his results using copper gauze electrodes in saline-soaked lint [2,8,9].
Nearly forty years elapsed between the first electrocardiogram [10] and Berger’s electroencephalogram, and another decade for EEG to be completely accepted. Why? To measure electrical activity from the brain non-invasively, it was necessary to have microvolt sensitivity, while electrocardiography (ECG) required only millivolt sensitivity. This thousand-fold difference meant that Berger had to not only improve the sensitivity of his string galvanometer, but also to consider electrochemical noise and the impedance of his electrodes. Sterilized zinc-plated needles were used for his first human studies. Later, he used thin lead-foil electrodes wrapped in flannel saturated with a 20% sodium-chloride solution [7]. Conventional electrodes today are in principle based on Berger’s original metal-foil electrodes.

To be completely accurate, EEG measures not the electrical activity in the brain, but the resulting fluctuations in electric potential at the scalp. It has been shown that these fluctuations can be used to study brain processes in-vitro, but what exactly causes these fluctuations? Some would argue that it is the net effect of the human brain’s 20-100 billion neurons firing at different times. Others have proposed instead that it is the result of ionic currents flowing in the brain. Or, it may be that we are observing in EEG a field which carries information from location-to-location. If it were possible, one potential solution to this question would be to start measuring at the single cell level, working up to the scalp level, to see how the electrical field changes. If the recording instrument had a high enough bandwidth, one would be able to observe and simulate the summation of fields from neurons firing. Deviations from the simulations would indicate if other processes needed to be taken account of. The difficulty in this experiment is that the tools used to measure at the single cell level differ greatly from EEG. In EEG, one typically considers microvolt-level signals between 0.1 and 100 Hz, whereas in single cell recordings, hundreds of millivolts are the norm at thousands of hertz.

The modern brain researcher has at his disposal a wide array of non-invasive instruments including EEG, magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and computed tomography (CT). It is important to understand how EEG fits in. While fMRI, PET and CT have excellent spatial resolution, they have poor temporal resolution – on the order of seconds. Incidentally, PET involves ingesting radioactive compounds and CT entails exposure to harmful radiation, so their suitability for non-clinical research is limited. EEG, and its relative, MEG, have comparatively poorer spatial resolution, but have temporal resolution on the order of milliseconds. MEG has some theoretical advantages, such as improved localization, however, the cost of installation and maintenance can be orders of magnitude greater than EEG. For these reasons, cognitive studies tend to based on either fMRI or EEG, or, more recently, both. EEG is the tool of choice when the aspect of interest is temporal in nature. Two notable examples, both scrutinized by Berger, are brain wave rhythms and the wave patterns associated with epileptic seizures.

Berger’s original intent was to obtain objective brain measurements on psychic phenomena such as telepathy. Though in his lifetime he failed to do so, along the way he revolutionized brain research. After nearly 80 years, EEG remains the primary method for diagnosing epilepsy, analyzing sleep disorder, assessing brain damage after a stroke, monitoring levels of consciousness in response to anesthesia and investigating brain death. Cognitive psychologists continue to use it to study memory, attentional and language processing. There has been a growing movement to treat disorders such as attention deficit disorder and depression using EEG-based biofeedback instead of medication. And, with the reduced cost and increased availability of computing power, and data storage, researchers increasingly interested in brain-computer interfaces based on the recognition of EEG patterns.

Our interest in improving EEG acquisition is an extension of successes we have had in the recognition of the brain wave representations of language [11-15]. A hybrid of cognitive psychology and machine learning, our work has been enabled not just by advances in computer hardware, but in particular by the quantity of data collected. While many in psychology continue to average across subjects, we have focused increasingly on individuals. However, because of waning interest and mental fatigue, it is often infeasible to run subjects beyond several hours. In addition, as discussed earlier, the necessity of scalp abrasion and application of a conductive paste severely limit the number and frequency of recording sessions. In order to continue supporting our analysis with data, it was necessary to consider alternatives to conventional EEG.

References
[1] H. Berger, "Über das elektroenkephalogramm des menschen," Archiv für Psychiatrie und Nervenkrankheiten, vol. 87, pp. 527-570, 1929.
[2] "EEG - ElectroEncephaloGraph," Biocybernaut Institute, [Online document], 2000, [cited 25 July 2003]. Available: http://biocybernaut.com/tutorial/eeg.html.
[3] T. W. Picton, S. Bentin, P. Berg, E. Donchin, S. A. Hillyard, R. Johnson, Jr., G. A. Miller, W. Ritter, D. S. Ruchkin, M. D. Rugg, and M. J. Taylor, "Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria," Psychophysiology, vol. 37, no. 2, pp. 127-152, 2000.
[4] B. Alizadeh-Taheri, R. L. Smith, and R. T. Knight, "An active, microfabricated, scalp electrode array for EEG recording," Sensors and Actuators A, vol. 54, pp. 606-611, 1996.
[5] C. J. Harland, T. D. Clark, and R. J. Prance, "Remote detection of human electroencephalograms using ultrahigh input impedance electric potential sensors," Applied Physics Letters, vol. 81, no. 17, pp. 3284-3286, 2002.
[6] W. Einthoven, "The string galvanometer and the measurement
of the action currents of the heart," Nobel Lecture, [Online document], 1925, [cited 30 July 2003]. Available: http://www.nobel.se/medicine/laureates/1924/einthoven-lecture.pdf.
[7] R. W. Thatcher, Functional neuroimaging : technical foundations. San Diego: Academic Press, 1994.
[8] O. D. Enersen, "Hans Berger," Who Named It?, [Online document], 2001, [cited 30 July 2003]. Available: http://www.whonamedit.com/doctor.cfm/845.html.
[9] J. D. Bronzino, "Principles of Electroencephalography," in The biomedical engineering handbook, J. D. Bronzino, Ed., 2nd ed. Boca Raton, FL: CRC Press, 2000.
[10] A. D. Waller, "A deomnstration on man of electromotive changes accompanying the heart's beat.," J. Physiol. (London), vol. 8, pp. 229-234, 1887.
[11] P. Suppes, Z. L. Lu, and B. Han, "Brain wave recognition of words," Proc Natl Acad Sci U S A, vol. 94, no. 26, pp. 14965-9, 1997.
[12] P. Suppes, B. Han, and Z. L. Lu, "Brain-wave recognition of sentences," Proc Natl Acad Sci U S A, vol. 95, no. 26, pp. 15861-6, 1998.
[13] P. Suppes, B. Han, J. Epelboim, and Z. L. Lu, "Invariance between subjects of brain wave representations of language," Proc Natl Acad Sci U S A, vol. 96, no. 22, pp. 12953-8, 1999.
[14] P. Suppes, B. Han, J. Epelboim, and Z. L. Lu, "Invariance of brain-wave representations of simple visual images and their names," Proc Natl Acad Sci U S A, vol. 96, no. 25, pp. 14658-63, 1999.
[15] P. Suppes and B. Han, "Brain-wave representation of words by superposition of a few sine waves," Proc Natl Acad Sci U S A, vol. 97, no. 15, pp. 8738-43, 2000.