EEG frequency and reaction time— A sequential analysis

Neuropsychologia, 1966,Vol. 4, pp, 41 to 48. PergamonPressLtd. Printedin England

EEG FREQUENCY AND REACTION A SEQUENTIAL ANALYSIS

TIME-

LENORE K. MORRELL Division of Neurology, Stanford University School of Medicine, Palo Alto, California, U.S.A. (Received 16 ApriI 1965)

Abstract-In a prolonged simple vigilance task with normal subjects who were not sleep deprived, trial-to-trial oscillations in EEG background frequency characteristics were noted. These are related to serial changes in reaction time to an aperiodic photic stimulus. Sequential analysis of the background frequency affords prediction of the probability of increased or decreased speed of response on successive trials, as well as an estimation of the likelihood of response failure.

1. INTRODUCTION THIS report is concerned with the relationship between the electroencephalographic (EEG) potentials recorded from persons performing simple tasks and the level of performance in those tasks. Specifically, it will deal with intra-individual variations in simple reaction time and also failure to respond to aperiodic light flashes as the behavioral indices. These will be related to certain frequency characteristics in the EEG in the one second epoch just prior to the presentation of a photic stimulus. A number of investigators have presented data on EEG characteristics present in the interval just preceding a stimulus and latency of the subsequent motor response to that stimulus [l, 2, 3, 4, 5, 6, 71. The present study differs from most of the previous ones in a number of respects, the two of immediate concern being the longer duration of the total task without interruption and the use of subjects who were not sleep-deprived. In addition, a sequential analysis of trial-to-trial changes in both EEG background states and behavior was accomplished.

2. METHOD The data are based upon 12 normal individuals (age range 19-31) all of whom presented in their resting electroencephalogram very prominent alpha activity. This was determined by inspection of the first 2 min of the EEG after requested eye closure; all subjects showed 8-12 c/s activity for at least 85 per cent of the epoch. They were seated in a lounge chair in a dark, sound-shielded room, and instructed to depress a microswitch as soon as a supraliminal brief light flash was detected. Subjects were asked to keep their eyes closed throughout the experiment, and informed that the session would take an hour at the maximum. Reaction times were measured with an electronic counter accurate to 1 msec, which was started with the light flash onset and terminated by the subject’s response. * This work was supported by NASA grant # NsG 215-62 Sl. 41

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Signals were delivered aperiodically, around a mean inter-stimulus interval of 12 set, with a range of 8-20 sec. Record lengths varied from 51 to 297 trials, the median being 153. The 12 records yielded a total of 1929 trials, of which 1847 are included in the present analysis. The remainder were excluded because of artifacts or ambiguity of EEG scoring. Multichannel bipolar recordings were made in all cases; in the present report only the data from the occipital lead (01 or 02 in the 10-20 system) will be considered. The reference electrode was an anterior lead in the homolateral parasagittal plane (central or frontal placement) on most occasions; for one subject, the reference was to linked ears. 2.1. Scoring of EEG The one second of the EEG visual lead just prior to the stimulus forms the basis of the present analysis. These were divided into three classes upon the basis of visual analysis: (a) alpha: continuous 8-13 c/s activity throughout the epoch; (b) slow : all activity in theta or delta bands (l-7 c/s) or at least three waves in these bands immediately preceding the stimulus; (c) mixed: most often, steady low voltage activity of mixed frequencies, or a mixture of alpha and low voltage more random frequencies. Only in this latter, “mixed” category was there any ambiguity in interpretation. This will be discussed later. 3. RESULTS 3.1. EEG background and reaction time There were 1847 trials which were scored for both EEG background and reaction time. Table 1 summarizes the grouped data, and gives the median, mean and range of reaction times for each of the three EEG categories delineated. Response failures were eliminated from the tabulations in Table 1. It is clear that reaction times are fastest when the signal is presented upon a background of continuous alpha activity (median=295 msec) and most delayed when measured against an EEG dominated by frequencies under 8 c/s (median= 439 msec). Variability of response, as measured by the range, is greatest in the trials preceded by slow wave activity. The “mixed” EEG category is characterized by an average reaction time in between the alpha and slow wave groups. A subject by subject analysis of these associations was done. For this computation response failures were included, being treated as infinitely long reaction times. Median reaction time values were calculated separately for each subject for each of the three EEG categories delineated. It was found that each subject showed the same ordering of median reaction times as that given in Table 1, which is based upon grouped data. For every Table 1. EEG background and reaction time* Background frequency

Reaction time (msec) Mean

Median

Range

Alpha Cn=1153)

299

295

111-891

Mixed (n=286)

351

320

141-930

Slow (n=269)

451

439

125-1426

* Response failures omitted from these tabulations.

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subject, median reaction times were fastest when measured against an alpha background, and slowest when measured against slow wave activity. The magnitudes of differences between medians in this alpha-slow comparison ranged from a low of 80 msec to a high of over a second. When trials with background alpha were compared with ‘mixed” activity, every subject showed the same direction of median difference in RT, with alpha background having the shorter RT’s. Individual median differences ranged from 10 to 295 msec; for the majority of subjects this difference was less than 80 msec.* 3.2. EEG background and response failure Table 2 summarizes the relationship between the incidence of response failure and the background frequency composition of the EEG in the one second prior to presentation of the photic stimulus. In the grouped data, failure to respond occurred in 139 trials, of which 110 (79 per cent) occurred coincident with slow wave epochs. Examined otherwise, when one Table 2. EEG background Background freauency Alpha

and response failure incidence

Number of response failures 2

Number of successful resuonses

Total (# trials)

1153

1155

Mixed

27

286

313

Slow

110

269

379

139

1708

1847

Total

Fro. 1. EEG tracings of normal young adult subject during a prolonged vigilance task. Designations for the upper 3 channels according to the IO-20 International Electrode Placement System. The signal marker on the fourth channel denotes the onset of a bright, brief flash of light delivered to the full visual field. The subject’s task was to respond to the detection of the light flash as quickly as possible by pressing a microswitch which activated another signal marker on the 5th channel. The latency of motor response in msec (as measured separately by an electronic counter) is written beside each response artifact, Note the burst of monorhythmic theta activity in the midportion of this figure. The subject failed to respond to the photic signal during the theta activity. However, signals delivered before or after the theta burst during portions of the record dominated by alpha rhythm were responded to promptly. * I am most indebted to Professor LINCOLN Mosm, executive head of the Dept. of Statistics, Stanford University, for the statistical evaluation of all data presented here. By the sign test all these differences are sign&ant at the 0.01 level.

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looks at the total of all slow wave trials, one finds 29 per cent of these are accompanied by failure to respond (110 out of 379 trials). Approximately 9 per cent of all trials characterized as “mixed” frequencies were accompanied by response failure. However, less than 0.02 per cent of all alpha background trials were associated with failure. Figure 1 illustrates these phenomena. The mid portion of the EEG tracing in this figure shows a burst of high voltage, almost monorhythmic theta activity of several seconds duration. A light flash delivered coincident with this activity failed to elicit signal detection behavior. Yet immediately prior to this failure and immediately afterward the signal was responded to rapidly. Figure 2 illustrates the fact that even a very brief burst of slow activity just prior to the signal may be associated with response failure. On the other hand, signals which occur only a second or two after the cessation of a theta burst may elicit prompt response; Figure 3 illustrates such a segment of record.

FIG. 2. Similar theta burst phenomenon, although of very brief (less than 1 set) duration. Also resulted in response failure.

These illustrations have been selected because they make quite clear by visual inspection the transitions from one pattern of background frequencies to another. Theta burst of the type illustrated occurred in a number of subjects in the present series; however somewhat more common were patterns of slow activity of sufficient voltage to be recognized by eye which did not have this highly rhythmic character.

Errors of commission, i.e. a motor response by the subject when no signal had been presented, were rare in the present series. However, all such errors occurred with slow wave activity in the background. Figure 4 provides an example of the EEG during a false response.

EEG FREQUENCY

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4. Error of commission. During a theta burst episode similar to those previously illustrated the subject may respond although no signal was delivered.

3.3. Sequential analysis Both EEG and reaction time data were tabulated in actual order of occurrence in real time. Successive pairs of trials were then evaluated in terms of both EEG background sequence and sequential changes in reaction time. These latter were scored as either increased, decreased, or unchanged from trial to trial. The three classes of EEG activity yielded nine possible sequence pairs (that is, alpha followed by alpha, or mixed activity, or slow; slow followed by alpha, mixed or slow, etc.). Analysis of the sequence of occurrence of such background states revealed that they the most likely next trial backtend to occur in “runs” , i.e. given an alpha background ground is likely to be alpha. Extending this type of analysis to “runs” of increasing length, the “mixed” background appears to be most unstable over time. That is, given two such successive EEG states in the 1 set period before stimulus is delivered, the likelihood of observing a third such state is less than chance. The alpha activity ranks first and slow wave activity occupies a middle position in terms of likelihood of maintenance of state over trials. To sum up the quantitative data on this point, 79 per cent of all measured EEG background at any point recur in the next succeeding trial, 64 per cent of all such measurements show a consistency of background state over at least three successive trials, 42 per cent over at least six successive trials, and 29 per cent show a constant EEG state (by these criteria) for ten or more successive trials. Although the above figures indicate considerable stability of EEG state from trial-totrial it is also clear from the data that fluctuation from state to state is a striking characteristic of the performing subject in a prolonged simple vigilance task setting. Such sudden shifts in EEG background patterns, it should be noted, occur in subjects who are not sleep deprived (hence the shift to slow wave activity is not a function of lack of sleep). Neither are the subjects alerted by the experimenter when such sleep-like waves characterize the record. Hence the return to rhythms considered related to more alert states is apparently spontaneous. Fluctuations of EEG background between alpha and slower and/or more random rhythms occur in all subjects in the present series. However there is a dependence upon time on task for the shifts to become frequent for some subjects. Trial to trial changes in the EEG backgrounds are clearly related to serial changes in reaction time to a photic stimulus. As may be seen in the pooled data in Table 3 when any given type of background activity (of the three classes already delineated) recurs in sequence there is almost equal likelihood for reaction time to increase or decrease from trial to trial.

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LENOREK.MORRELL Table 3. Sequential analysis of EEG frequency and directional changes in reaction time RT faster on 2nd trial

RT slower on 2nd trial

RT equal

1

2

3

Background sequence

Response failures 4 1st trial (i+)

C#)

(%)

(#I

(%)

(#)

(%I

411

41.2

422

49.1

24

2.8

2

5 2nd trial (#)

alpha-alpha;

n=857

alpha-mixed;

n= 138

62

44.9

13

52.8

3

2.2

0

0

alpha-slow ;

n=128

23

19.9

105

82.0

0

0

1

29

mixed-alpha;

n=126

82

65.1

44

34.9

0

0

I

0

mixed-mixed ; n = 109

47

43.1

53

48.6

9

8.3

9

11

12

16.4

56

16.1

5

6.8

4

17

115

85.2

18

13.3

2

1.5

39

1

35

53.8

23

35.4

I

10.8

12

9

76

42.5

73

40.1

30

16.8

51

63

mixed-slow ;

n= 13

slow-alpha;

n=135

slow-mixed;

n=

slow-slow;

n=179

65

2

Each 1 set epoch just prior to photic signal is compared with the immediately succeeding pre-signal 1 set epoch of EEG. Grouped data, based upon 1810 pairs of successive trials. In columns 1, 2 and 3, response failures are treated as infinitely long reaction times and are included in the tabulations. The number of failures are given separately in columns 4 and 5.

The most prominent changes are to be seen when there are sudden shifts between alpha rhythm and slow wave epochs. Taking as a base all successive pairs with alpha present on the first and slow wave activity prominent on the next trial, 82 per cent of all reactions are longer on the second of the pair. On the other hand, 85 per cent of reaction times are shorter on the second trial of a pair where the EEG goes from a slow wave background to steady alpha rhythm. That the mixed category of EEG background does probably relate to transitional states of consciousness is seen by the fact that when the transition is from mixed to slow activity, in 77 per cent of such instances reaction time is longer on the second trial. If the transition is from mixed to alpha activity, in 65 per cent of instances reaction time is faster on the second of the pair. The effects hold for an analysis for the variations within each individual record as well as for the grouped data.* A similar analysis of complete response failure in terms of these EEG transitions was made and the findings are in accord with the conclusions reached above. The most striking trial-to-trial shifts in the incidence of response failure occur when there are sudden changes between alpha and slow activity (see Figs. l-4). Response failure is most likely to occur in those sequences of two (or more) successive slow frequency pre-signal epochs. Columns 4 and 5 of Table 3 summarize the findings. * For each subject, an index of trend was computed which was, substantially, a correlation coefficient between a Score X and a Score Y. X was - 1 as background rhythms shifted from alpha to either mixed or slow frequencies, and also where mixed frequencies were followed by slow ones on the second of two trials; X=0 when they were the same on successive trials; X= + 1 when the trial-to-trial shift was from slow to either mixed or alpha, or mixed to alpha. Y was -1, 0 or +l as the reaction time was longer, equal or shorter on the second of two trials. For every one of the 12 subjects this index was positive. By the sign test this established the statistical significance of the associations.

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4. DISCUSSION The main finding of the present investigation is that fluctuations in the efficiency of the human performing a simple vigilance task (as measured by speed of motor response) are related to phasic shifts in background EEG frequency characteristics. If the task is long enough, and the subject is not interrupted, such oscillations in both behavioral and electrographic indicators of vigilance level may be seen even when the subject is not sleep-deprived. These apparently spontaneous shifts would seem to be characteristic of persons in relatively restricted, monotonous environments. Previous work with a simple habituation paradigm provided other evidence for the presence of central oscillatory processes [8]. In that study, the duration of electrographic arousal to photic stimuli as a function of stimulus repetition was the measure of concern. There are a number of experiments which offer evidence that human performance in vigilance tasks undergoes decrement as a function of time on task (see [9] for a summary). It is characteristic of these studies that rather large chunks of time have been selected to plot the effect, often 3 hr per point. This has been done, perhaps, because many such studies employ very infrequent critical signals. The present study indicates that when a trial-totrial analysis of performance is made, under conditions of frequent signal presentation, there are fluctuations in efficiency which are associated with shifts in EEG frequency. Many of the theories advanced to describe performance decline with time on task (fatigue, distraction, internal inhibition, habituation) do not appear to take into account the spontaneous increases in performance level which recur over time (see MCCORMACK[IO] for a review of these theories and a discussion of the similarities between reaction time studies and more conventional vigilance tasks). OSWALD[I l] has called attention to this tendency in the vigilance literature to fail to deal with response fluctuation, stating that “it needs to be more generally realized that the fall of cerebral vigilance during such tasks does not occur as a steady decline but occurs briefly over and over again, with frequent upsurges of cerebral vigilance to, once more, a high level . . . ” (p. 163). WILLIAMS,LUBINand G~ODNOW[12] report that a critical factor in selecting behavioral measures most sensitive to the effects of sleep deprivation is the length of time, without interruption, that a repetitive task is to be performed. They noted that response failures in prolonged tasks were correlated with diminution of alpha activity in the one second epoch prior to the stimulus. The same prescription seems appropriate for studying the subject who is not sleep-deprived, if the experimental goal is to study in detail the relation between EEG and behavior. Many such studies have used brief runs of trials, separated by rest periods [2,4,6 71. By thus “re-setting” the subject’s level of alertness, the experimenter may alter or perhaps obscure both the behavioral and electrographic measures of potential interest. WILLIAMSet al. [4] reported that the one second epoch of the EEG prior to the signal onset gave the best correlation with reaction time, when compared with certain other time epochs prior to and succeeding the signal. The EEG criterion was a simple count of the number of waves in that epoch. We have not yet attempted a proper analysis of the parametric aspects of selecting the length of background record to examine. However, it would appear from the present data that the 1 set period has predictive utility. Sudden shifts of inter-trial background rhythms from alpha to slow waves, where the slow activity had commenced only 1 set prior to signal delivery, were noted repeatedly. The reverse type of

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LENOREK. MORRELL

abrupt transition was also frequently seen. Yet the 1 set epoch under these conditions appeared a sufficient duration of maintenance of central state in order to permit prediction of behavioral change. As alpha rhythms diminish in a record, they may give way to EEG patterns indicative of either increased or decreased alertness. It is fairly simple to recognize, visually, those instances of high voltage slow wave activity. Visual analysis of records would seem to be much less reliable in attempts to distinguish the low voltage fast pattern (generally viewed as “activation”) from the flattened random activity which may contain low voltage slow components. The latter is typical of drowsiness and light sleep. The “mixed” category in the present report suffers from this limitation of ambiguity. We are at present repeating this type of experiment with the aid of a period analysis program on the LINC computer, in order to better specify frequency components which the eye cannot resolve. REFERENCES 1. LANSING,R. W., SCHWARTZ,E. and LINDSLEY,D. B. J. exp. Psychol. 58, 1, 1959. 2. FEDIO, P., MIRSKY, A. F., SMITH, W. J. and PARRY, D. Electroenceph. clin. Neurophysiol. 13, 923, 1961. 3. DUSTMAN,R. E., BOSWELL,R. S. and PORTER,P. B. Science 130, 3529, 1962. 4. WILLIAMS,H. L., GRANDA,A. M., JONES,R. C., LUBIN, A. and ARMINGTON,J. C. Electroenceph. clin. Neurophysiol. 14, 64, 1962. 5. BIERNER,B. Alpha depression and lowered pulse rate during delayed reactions in a serial reaction test. Acta physiol. scnnd. 193, 19, Suppl. 65, 1, 1949. 6. SURWILLO,W. W. The relation of decision time to brain wave frequency and to age. Electroenceph. clin. Neurophysiol. 16, 510, 1964. 7. HERMELIN,M. and VENABLES,P. H. Reaction time and alpha blocking in normal and severely subnormal subjects. J. exp. Psycho/. 67, 365, 1964. 8. MORRELL,L. and MORRELL,F. Non-random oscillation in the response-duration curve of electrographic activation. Electroenceph. clin. Neurophysiol. 14, 724, 1962. 9. MCGRATH, J. J., HARABEDIAN, A. and BUCKNER,D. N. Review and Critique oj’the Literature on Vigilance Performance. Human Factors Research, Inc., Los Angeles, 1959. 10. MCCORMACK,P. D. A two-factor theory of vigilance. Br. J. Psychol. 53, 357, 1962. 11. OSWALD,I. Seeping and Waking. Elsevier Publishing Co., Amsterdam, N.Y., 1962. 12. WILLIAMS,H. L., LUBIN, A. and GOODNOW,J. J. Psycho/. Monogr. 73, 484, 1959. RBsumC-Dans un test de vigilance simple et prolong& chez des sujets normaux n’ayant pas subi la d&privation hypnique, on notait des oscillations d’un essai B l’autre dans les caracttristiques de frtquence du rythme de fond EEG. Ces oscillations sont likes ti des modifications sbielles dans le temps d’rtaction & un stimulus photique non ptriodique. L’analyse sCquentielle des frequences du rythme de fond permetait une prCdiction de la probabilitk de I’augmentation et de la diminution de vitesse des reponses aux essais successifs, de m&me qu’une estimation de la probabilitt des absences de rkponse.

Zusammenfassung-Gesunde Normalpersonen ohne Schlafdefizit wurden einem unkomplizierten, aber prolongierten Aufmerksamkeitstest unterworfen. Ein dabei abgeleitetes EEG zeigte charakteristische Frequenzschwankungen des Grundrhythmus. Sie gingen Hand in Hand mit der jeweiligen Testbelastung. Es ergab sich ein Zusammenhang zwischen den wechselnden Reaktionszeiten und aperiodisch gesetzten Lichtreizen. Wenn man die Grundrhythmusfrequenz im LLngsschnitt analysierte, konnte man voraussagen, ob die Testbelastung einen Anstieg oder Abfall der Hirnstromgeschwindigkeit bewirkte, oder ob eine EEGAntwort ausblieb.