M. Indra

Following complex rhythmical acoustical patterns by tapping

Rhythmic acoustical sequences consisting of patterns of various complexity (number of intervals in pattern) were generated and subjects were asked to synchronize with them by finger tapping. Deviations between onset of stimuli and onset of response were measured. It has been confirmed that tapping precedes the onset of stimuli by about 20-30 ms. The duration of this anticipatory period increased with an increase in stimulus complexity to the limit of three intervals in pattern. This finding points to a systematic error in timing of rhythmic motor reactions during acoustical following. Its degree depends to a certain limit on the nature of the stimuli. Subjects are subjectively aware of this error.

Tracking errors related to cardiac cycle: a new approach

One-dimensional manual tracking was investigated in relation to cardiac activity. The task of the subjects was, by means of a joy-stick, to maintain a spotlight between two vertical lines moving horizontally across an oscilloscope screen. Error incidence was time-locked with respect to the cardiac cycle. Error rate was higher for faster (2-s trial periods) than for slower (3-s trial periods) target movement. Using linear-ramp and sinusoidal movements, it was demonstrated that error incidence is associated with positive and negative cardiac acceleration.

Tracking irregular acoustic patterns by finger tapping

Acoustical sequences were divided by time intervals of various durations, and the performance of finger tappings following the stimuli were recorded. The results proved that it is difficult to synchronize finger tapping with irregular acoustical patterns. The subjects tended to transform the irregular stimulus patterns into more regular response patterns shifted toward interval proportions close to 1:1 or 2:1.

EEG SPECTRAL CHANGES DURING ONE-DIMENSIONAL HAND-TRACKING '

For our study of change in spectral power in the EEG (alpha and beta bands) when subjects were on and off target during one-dimensional hand-tracking, 11 healthy paid subjects, 8 men and 3 women of mean age 23.4 yr., volunteered to participate in the experiments. During 3-min. tracking task (1) the subject had to keep a joystick on a light-spot inside the given target (two vertical bars) which moved horizontally across the CRT screen. The instant a subject failed to keep the light-spot within target limits was scored as a tracking error (OUTevent). The return into the target was evaluated as error correction (IN event). EEG was recorded during the whole series of 8 tracking tasks, differing in target size (10 and 20 rnm), type of target movement (continuous and interrupted), and target speed (constant and feedback modified) (2) from O,, 0, and C,, C, (according to the 10120 system of electrode locations) by means of nonpolarizing AgtAgC1 electrodes. Linked earlobe electrodes served as the common reference. The power spectra in alpha and beta bands were chosen as main spectral characteristics of EEG as several subjects showed suppression of EEG alpha activity recorded on 0, and 0, positions after tracking error in pilot experiments. To describe the relationship between the tracking (IN/OUT) events and occurrence of EEG alpha and beta activity, the following method was adopted. Using a Fast Fourier Transformation (FFT) procedure, pairs of power spectra were calculated in averaged l-sec. EEG intervals with IN and OUT events located in their center. Then the ratios of power spectra for the alpha and beta frequency bands to the total power spectra were calculated. The frequency bands were set to 8-13 Hz for alpha and 13-24 Hz for beta activity. It followed from the comparisons of all values obtained (pooled together as the results were similar on all the tasks) that in 65% of cases the power of EEG alpha activity on 0, and 0, was lower immediately after OUT events than before them (by 5.8% p< .05), whereas in 62% the power of beta activity became lower on C, and C, immediately after IN events than before them (by 1.2%). It might be assumed that stereotyped manual cracking is often combined with a prevalence of alpha activity in EEG whereas on an out-ol-larger s~tuation voluntary effort toward error correction is accompanied by an increase of power in be~a band.

EEG ALPHA ACTIVITY PROCESSING AND MODELLING

Abstract On-line as well as off-line EEG alpha activity evaluation is described. The methods of detection of EEG alpha activity and its scoring are mentioned. A filtered EEG activity is fed into a computer where by sequential approach the value of median of probability distribution of their local extremes has been estimated. This value serves as a threshold for the threshold detection itself. Integral detection method is based on the computation of the area under this filtered signal. Model of alpha and non-alpha alternation based upon the analogy with the queueing process is suggested.