EEG power spectrum analysis for monitoring depth of anaesthesia during experimental surgery

The first attempts to introduce computerized power spectrum analysis of the electroencephalogram (EEG) as an intraoperative anaesthesia monitoring device started approximately 30 years ago. Since that time, the effects of various anaesthetic agents, sedative and analgesic drugs on the EEG pattern have been addressed in numerous studies in human patients and different animal species. These studies revealed dose-dependent changes in the EEG power spectrum for many intravenous and volatile anaesthetics. Moreover, EEG responses evoked by surgical stimuli during relative light levels of surgical anaesthesia have been classified as 'arousal' and 'paradoxical arousal' reaction, previously referred to as 'desynchronization' and 'synchronization', respectively. Contrasting reports on the correlation between quantitative EEG (QEEG) variables derived from power spectrum analysis (i.e. spectral edge frequency, median frequency) and simultaneously recorded clinical signs such as movement and haemodynamic responses, however, limited the routine use of intraoperative EEG monitoring. In addition, the appearance of EEG burst suppression pattern and isoelectricity at clinically relevant concentrations/doses of newer general anaesthetics (i.e. isoflurane, sevoflurane, propofol) may have weakened the dose-related EEG changes previously reported. Despite these findings, the EEG power spectrum analysis may still provide valuable information during intraoperative monitoring in the individual subject. The information obtained from EEG power spectrum analysis may be further supplemented by newer EEG indices such as bispectral index and approximate entropy or other neurophysiological monitors including auditory evoked potentials or somatosensory evoked potentials.     

Alpha-rhythm of the EEG in directed attention

Degree of manifestation and synchronization of rhythmic alpha-range components in various cortical areas was studied in healthy adult subjects in conditions of controlled computer experiment by spectral-correlation method of EEG analysis during directed attention previous to discrimination of tactile, auditory and visual stimuli. The obtained results show that in preparation for discrimination of heteromodal stimuli, specific reconstructions take place of alpha-range electrical activity determined by signal modality. These changes are expressed in a local increase of the degree of synchronization of alpha oscillations in those projection and associative brain areas which are connected with the analysis of the given stimuli. Functional significance of these reconstructions is seen in the fact that they are significantly more expressed at correct recognition. A hypothesis is suggested about participation of the alpha-range in neurophysiological mechanisms of directed attention.     

Spatial organization of EEG activity during active hyperventilation (cyclic breath) I. general patterns of changes in brain functional state and the effect of paroxysmal activity

Changes in spatial organization of EEG activity were analyzed in 44 humans during active 1-h hyperventilation using cyclic or circular breath (CB) technique similar to rebirthing breath technique. The dynamics of different indices was recorded each 5 min (using 12 time slots). A double-humped pattern of changes in spatial organization indices (linear processes) and spatial disorder (nonlinear processes) of biopotentials: an initial decrease within 1 to 20–30 min and a second one from 35–40 min to the end of session. A complex dynamics of spatial frequency processes (coherence and spectral power of biopotentials) with different pattern of changes within narrow frequency EEG bands. The dynamics of the spatial organization of EEG indices proved to depend on the intensity of hyperventilation-induced paroxysmal activity. The indices of spatial synchronization and disorder of biopotentials as well as low frequency β-activity (16.00–22.50 Hz) decreased more at the background of high rather than low paroxysmal activity, while the low frequency components (Δ and Θ) and high frequency α-activity (11.25–12.50 Hz) increased more. The obtained data are considered in terms of specific consciousness state induced by CB.     

Detection of moments of operation of functional units in the human cortex using a new method of EEG analysis]

A new approach to EEG processing was proposed. The method allows the poorly predicted points on an EEG recording to be found, which are interpreted as the moments when certain functional units of the brain are engaged or disengaged. The functional units may be probably represented by columns or supercolumns. The method was tested in EEG recordings when the visual illusions were induced in a subject by a rhythmic photostimulation. The method seems to be rather promising.     

Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation (tACS)

Oscillatory brain activities are considered to reflect the basis of rhythmic changes in transmission efficacy across brain networks and are assumed to integrate cognitive neural processes. Transcranial alternating current stimulation (tACS) holds the promise to elucidate the causal link between specific frequencies of oscillatory brain activity and cognitive processes. Simultaneous electroencephalography (EEG) recording during tACS would offer an opportunity to directly explore immediate neurophysiological effects of tACS. However, it is not trivial to measure EEG signals during tACS, as tACS creates a huge artifact in EEG data. Here we explain how to set up concurrent tACS-EEG experiments. Two necessary considerations for successful EEG recording while applying tACS are highlighted. First, bridging of the tACS and EEG electrodes via leaking EEG gel immediately saturates the EEG amplifier. To avoid bridging via gel, the viscosity of the EEG gel is the most important parameter. The EEG gel must be viscous to avoid bridging, but at the same time sufficiently fluid to create contact between the tACS electrode and the scalp. Second, due to the large amplitude of the tACS artifact, it is important to consider using an EEG system with a high resolution analog-to-digital (A/D) converter. In particular, the magnitude of the tACS artifact can exceed 100 mV at the vicinity of a stimulation electrode when 1 mA tACS is applied. The resolution of the A/D converter is of importance to measure good quality EEG data from the vicinity of the stimulation site. By following these guidelines for the procedures and technical considerations, successful concurrent EEG recording during tACS will be realized.     

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator''s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.     

Neural Mechanisms Underlying Stop-and-Restart Difficulties: Involvement of the Motor and Perceptual Systems

The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time) of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (∼100 ms) stop-to-restart intervals (SRSI), and an increased probability of difficulties after longer (>200 ms) SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs) in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms), the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM) excitability. Finally, we recorded electroencephalogram (EEG) activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms), weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150–200 ms), because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results suggest that skilled motor behavior is subject to various constraints in not only motor, but also perceptual (and attentional), systems.     

Characteristics of the spatial organization of oscillations of brain bioelectric potentials in adolescents

Adolescence is characterized by an intense formation of interregional interaction of cortical areas. In this period, the activity of deep brain structures is significantly reorganizing and cortical-subcortical interaction is augmenting. Our objective was to assess the pattern of changes in the spatial structure of brain bioelectric potentials with age and characteristics of these structures in adolescents. For this purpose, studies of EEG were conducted in 230 subjects of both sexes aged 4 to 35 years. We quantified the interconsistent changes in correlations of oscillations of bioelectric potentials in 20-lead EEG, using the integrated index VOL. Age-related changes in the consistency of EEG correlations were analyzed both in the background state and during verbal activity (comprehension of texts in Russian and in English). Cognitive tasks were performed by subjects older than 8 years. It was discovered that spatial synchronization of EEG processes both in the background state and during cognitive tasks increased with age, but, after 20 years, the rate of changes decreases significantly. In adolescence (12–17 years), sex differences appear in the correlation of EEG processes between the left and right hemispheres in subjects performing verbal tasks. We observed saltatory changes in VOL indices in 12- to 14-year-old boys, whereas in girls of the same age, reorganization of systemic brain activity goes more gradually 1.5–2 years in advance.     

Amplitude and phase relations of electrocortical waves regulated by transhypothalamic dopaminergic neurones: A test for a linear theory

We have previously proposed that electrocortical activity (EEG) arises as a manifestation of linear waves generated by resonance among telencephalic neurones, and that this activity is controlled in part by ascending neurones from the brain-steim, which regulate the damping of each resonance. The presentexperiments focus on a specific class of ascending neurones, the mesotelencephalic dopaminergic cells, because these cells are thought to mediate important psychological effects, and are conveniently subject to selective lesion. A critical test of the theory is undertaken, by performing selective unilateral lesion, assessing the changes in the power spectrum of the EEG attributable to lesion, and determining whether the changes in phase of the EEG correspond to that predicted from the changes in power. Results support the theory, although the model order applicable in these experiments is inadequate. The consequences of these findings for automata theory, linear network theory and their application to mammalian brains are briefly discussed.     

Increase of slow periodic modulation of EEG in a patient with Alzheimer's disease.

The recently described slow oscillations of amplitude of theta and alpha waves of the EEG (with a frequency below 0.08 Hz) in healthy subjects are attributed to the autonomic nervous system with control at the brain stem level. In the present pilot study, the slow brain rhythms were analyzed in a patient with Alzheimer's disease and were compared to a healthy subject. Dynamic analysis of the EEG was performed using time-frequency mapping which gives simultaneous time and frequency representation of the brain signal. This method comprises a transform of the filtered EEG signal into its analytic form and application of the Wigner distribution modified by time and frequency smoothing. It has been shown that the envelope of both theta and alpha activities oscillates at 0.04 Hz and 0.07 Hz in the healthy subject and at 0.03 Hz and 0.06 Hz in a patient with Alzheimer's disease. The amplitude of the slow oscillations of theta activity was substantially higher in the patient with Alzheimer's disease as compared with the healthy subject. It is being proposed that the increase of slow brain rhythms in the patient with Alzheimer's disease reflects an abnormal activity of the autonomic nervous system. However, the underlying pathophysiological mechanisms need to be further studied.     

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of \(\beta\) and \(\gamma\) rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.     

The effects of sloped surfaces on locomotion: an electromyographic analysis

Investigations using quadrupeds have suggested that the motor programs used for slope walking differ from that used for level walking. This idea has not yet been explored in humans. The aim of this study was to use electromyographic (EMG) signals obtained during level and slope walking to complement previously published joint angle and joint moment data in elucidating such control strategies. Nine healthy volunteers walked on an instrumented ramp at each of five grades (-39%, -15%, 0%, +15%, +39%). EMG activity was recorded unilaterally from eight lower limb muscles (gluteus maximus (GM), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF), semimembranosus (SM), soleus (Sol), medial gastrocnemius (MG), and tibialis anterior (TA)). The burst onset, duration, and mean activity were calculated for each burst in every trial. The burst characteristics were then averaged within each grade and subject and submitted to repeated measures ANOVAs to assess the effect of grade (alpha=0.05, a priori). Power production increased during upslope walking, as did the mean activity and burst durations of most muscles. In this case, the changes in muscle activity patterns were not predictable based on the changes in joint moments because of the activation of biarticular muscles as antagonists. During downslope walking power absorption increased, as did knee extensor activity (mean and duration) and the duration of the ankle plantarflexor activity. The changes in muscle activity during this task were directly related to the changes in joint moments. Collectively these data suggest that the nervous system uses different control strategies to successfully locomote on slopes, and that joint power requirements are an important factor in determining these control strategies.     

The Use of Actimetry to Assess Changes to the Rest–Activity Cycle

The endogenous circadian oscillator (the body clock) is slow to adjust to altered rest–activity patterns. As a result, several negative consequences arise during night work and after time‐zone transitions. The process of adjustment can be assessed by measurements of the sleep electroencephalogram (EEG), core temperature or melatonin secretion, for example, but these techniques are very difficult to apply in field studies, and make very great demands upon both experimenters and subjects. We have sought to establish if the activity record, measured conveniently and unobtrusively by a monitor attached to the wrist, can be treated in ways that enable estimates to be made of the disruption caused by changes to the rest–activity cycle, and the process of adjustment to them. In Part A, we describe the calculation and assessment of a series of “activity indices” that measure the overall activity pattern, activity when out of bed or in bed, or the activity in the hours adjacent to going to bed or getting up. The value of the indices was assessed by measuring changes to them in subjects undergoing night work or undergoing time‐zone transitions. In both cases, there is a large body of literature describing the changes that would be expected. First, night workers (working 2 to 4 successive night shifts) were investigated during rest days and night shifts. The indices indicated that night work was associated with lower activity when the subjects were out of bed and higher activity when in bed. Some indices also measured when subjects took an afternoon nap before starting a series of night shifts and gave information about the process of adjustment to night work and recovery from it. Second, in studies from travelers crossing six or more time zones to the east or west, the indices indicated that there were changes to the rest–activity cycle immediately after the flights, both in its overall profile and when activity of the subjects in bed or out of bed was considered, and that adjustment took place on subsequent days. By focusing on those indices describing the activity records during the last hour in bed (LHIB) and the first hour out of bed (FHOB), some evidence was found for incomplete adjustment of the body clock, and for differences between westward and eastward flights. In Part B, the battery of indices are applied to the activity records of long‐haul pilots, whose activity patterns showed a mixture of effects due to night work and time‐zone transitions. Actimetry was performed during the flights themselves and during the layover days (which were either rest or work days). The indices indicated that all pilots had disrupted rest–activity cycles caused by night flights, and that there were added problems for those who had also undergone time‐zone transitions. Rest days were valuable for normalizing the activity profile. For those pilots who flew to the west, adjustment was by delay, though not all aspects of the rest–activity cycle adjusted immediately; for those who flew to the east, some attempted to advance their rest–activity cycle while others maintained home‐based activity profiles. The indices indicated that the activity profile was disrupted more in those pilots who attempted to advance their rest–activity cycle. We conclude that objective estimates of the disruption caused to the rest–activity cycle and the circadian system can be obtained by suitable analysis of the activity record.     

Intensity and type of EEG reactions to a hyperventilation test and their relationship with changes in patients’ subjective state of health and some characteristics of the initial EEG pattern

Some EEG characteristics at rest and after a 3-min hyperventilation test (HVT) were studied in 100 adult patients along with the changes in their current subjective state of health, assessed with the help of a special three-stage survey and a questionnaire. Our findings demonstrated a clear correlation between the rate of changes in the current subjective state of health of a patient, the average number of observed symptoms, and the intensity of the EEG reaction. With regard to the methodological aspect, we recorded a difference in the assessment of subjective data obtained through the different types of survey. The frequency of two types of high-amplitude slow waves during the HVT (recorded in 21% of patients), as well as of paroxysmal epileptiform activity, was studied, and their correlation with the clinical manifestations of epilepsy was analyzed. Our study showed that the intensity of the general EEG reaction to the HVT is correlated with the presence of EEG disorders, such as minor bursts in the initial EEG pattern; the probability of high-amplitude slow activity during the HVT is especially high if these minor bursts are recorded at rest. We also noted a certain relationship between the intensity of hyperventilation-related EEG changes (and, therefore, the rate of changes in the current subjective state of health of a patient) and characteristics of the initial pattern, such as α-rhythm disorders and an increase in the intensity of slow or slow and frequent activity at rest.     

Epileptic Discharges Affect the Default Mode Network – fMRI and Intracerebral EEG Evidence

Functional neuroimaging studies of epilepsy patients often show, at the time of epileptic activity, deactivation in default mode network (DMN) regions, which is hypothesized to reflect altered consciousness. We aimed to study the metabolic and electrophysiological correlates of these changes in the DMN regions. We studied six epilepsy patients that underwent scalp EEG-fMRI and later stereotaxic intracerebral EEG (SEEG) sampling regions of DMN (posterior cingulate cortex, Pre-cuneus, inferior parietal lobule, medial prefrontal cortex and dorsolateral frontal cortex) as well as non-DMN regions. SEEG recordings were subject to frequency analyses comparing sections with interictal epileptic discharges (IED) to IED-free baselines in the IED-generating region, DMN and non-DMN regions. EEG-fMRI and SEEG were obtained at rest. During IEDs, EEG-fMRI demonstrated deactivation in various DMN nodes in 5 of 6 patients, most frequently the pre-cuneus and inferior parietal lobule, and less frequently the other DMN nodes. SEEG analyses demonstrated decrease in gamma power (50–150 Hz), and increase in the power of lower frequencies (<30 Hz) at times of IEDs, in at least one DMN node in all patients. These changes were not apparent in the non-DMN regions. We demonstrate that, at the time of IEDs, DMN regions decrease their metabolic demand and undergo an EEG change consisting of decreased gamma and increased lower frequencies. These findings, specific to DMN regions, confirm in a pathological condition a direct relationship between DMN BOLD activity and EEG activity. They indicate that epileptic activity affects the DMN, and therefore may momentarily reduce the consciousness level and cognitive reserve.     

Utilization of factor analysis for the evaluation of ontogenetic and functional changes in the electrical activity of the brain

The tendency of the index, amplitude and its dispersion during the year is opposite to the age-dependent tendency of changes in these indices and points to a chronic component of mental fatigue. It is very important that in the second half of the school year the dispersion of the EEG amplitude increases in all sections of the brain and in all frequency ranges: an increase in EEG amplitude dispersion in parallel with an increase in the amplitude itself indicates that the process of synchronization and the ultraliminal inhibition connected with it in different sections of the brain and in various frequency ranges of the EEG is not stagnant or inert but mobile and instable and can therefore be relatively easily removed by special hygienic measures. As an example of the latter we can mention the use of a special regime of exercise (Yu. M. Pratusevich et al., 1975), addition of supplementary factors of nutrition to the diet of the time the children spend in the fresh air engaging in outdoor games (Yu. M. Pratusevich, 1964).     

Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes

The dynamical and physiological basis of alpha band activity and 1/f^β noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/f^β noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (β) of the power spectrum (1/f^β). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/f^β noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG.     

Eight criticisms not to make about group selection

Group selection, which was once widely rejected as a significant evolutionary force, is now accepted by all who seriously study the subject. There is still widespread confusion about group selection, however, not only among students and the general public, but among professional evolutionists who do not directly study the subject. We list eight criticisms that are frequently invoked against group selection, which can be permanently laid to rest based upon current knowledge. Experts will always find something to critique about group selection, as for any important subject, but these eight criticisms are not among them. Laying them to rest will enable authors to openly use the term group selection without being handicapped during the review process.     

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

ObjectiveEpileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats.MethodsSeizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal periods were then recorded and analyzed to predict epileptic seizures. Epileptic seizures were predicted by calculating an index in consecutive windows of EEG signal and comparing the index with a threshold. In this work, a newly proposed dissimilarity index called Bhattacharyya Based Dissimilarity Index (BBDI), dynamical similarity index and fuzzy similarity index were investigated.ResultsBBDI, dynamical similarity index and fuzzy similarity index were examined on case and control groups and compared to each other. The results show that BBDI outperforms dynamical and fuzzy similarity indices. In order to improve the results, EEG sub-bands were also analyzed. The best result achieved when the proposed dissimilarity index was applied on Delta sub-band that predicts epileptic seizures in all rats with a mean of 299.5 s.ConclusionThe dissimilarity of neural network activity between reference window and present window of EEG signal has a significant increase prior to an epileptic seizure and the proposed dissimilarity index (BBDI) can reveal this variation to predict epileptic seizures. In addition, analyzing EEG sub-bands results in more accurate information about constituent neuronal activities underlying the EEG since certain changes in EEG signal may be amplified when each sub-band is analyzed separately.SignificanceThis paper presents application of a dissimilarity index (BBDI) on EEG signals and its sub-bands to predict PTZ-induced epileptic seizures in rats. Based on the results of this work, BBDI will predict epileptic seizures more accurately and more reliably compared to current indices that increases epileptic patient comfort and improves patient outcomes.     

Continuous changes in electroencephalographic topograms and auditory reaction time during simulated 21 ATA (atmospheres absolute) heliox saturation dives

The present study was designed to investigate EEG changes during two simulated 21 ATA hyperbaric heliox saturation dives. Four professional male divers were attached to 13 electrodes over the scalp and compressed to 21 ATA by helium. Throughout the dives, EEGs were measured and stored on a magnetic FM tape recorder to calculate electroencephalographic topograms. Three patterns of slow wave increase were clearly distinguished by the electroencephalographic topogram during compression. The first pattern was caused by trains or bursts of theta waves which spread from the frontal region to the central region of the scalp. This pattern occurred at comparatively shallow depth and showed the slowest recovery during the bottom stage among three patterns. The second pattern found in two divers was caused by frontal midline theta bursts, which showed maximum activity at F2. The frontal midline theta bursts were augmented by a reaction time task. The third pattern was caused by vertex sharp waves which indicated a transition from sleep stage 1 to sleep stage 2. Neurophysiological meanings of these EEG patterns as well as the relationship between EEG changes and other indices were discussed.