Results

In order to compare Marwan et al. (2002) and our algorithm, we plot the recurrence matrix, the extracted line of synchrony, see figure 5.10a, and the averages, equation 5.15, using these distortion functions, see figure 5.10b. The function correctly identified by our method improves the shape of the average (i.e. its shape is closer to the underlying signal) while a non-optimal path adds further distortions rather than recovering the original signal. Because we directly construct the distortion function for the artificial data, we can define a distance measure ∆(φ,φ)ˆ that, for each point in the recovered ˆφ, returns its distance to the closest point in the real distortion functionφ. Because we encounter high noise-levels in realistic settings, it is important that the algorithms are stable against noise. We present in figure 5.10c the result of comparing both algorithm with varying noise-level. Our method (red) performs significantly better in this task but both algorithms are relatively stable with respect to the level of noise in the data.

Finally, we show examples for the temporal averaging based on the extracted LOS. In fig-ure 5.11a, the shape of the original signal can be fully recovered even from very noisy data (gray

5.8 Summary

Figure 5.11: (a) The template from which the artificial data was generated is recovered by our method (red) while the conventional average (blue) fails to produce some of the peaks. (b) Real ERP-data from 134 trials at electrode location CPz. Our resyn-chronization method shows more pronounced and better localized peaks (e.g. P300).

Embedding parameters were m=10,τ=20,FAN=100, time-scale of single trials was transformed for plotting according to the described algorithm.

curves are single trials). In contrast, the conventional pointwise average (blue) washes out some of the peaks.

The data presented here was taken from the priming experiment described in chapter 4. We applied a bandpass filter with cutoff frequencies 0.5 and 20 Hz before data analysis. The results of applying our method to the data is exemplified in figure 5.11b. Our average produces pronounced peaks in the expected time window (e.g. P300) that are better visible and more tightly localized in time than when using conventional averaging.

5.8 Summary

Traditional averaging procedures upon which the entire field of event-related potential research is relying have certain drawbacks, we addressed in the current chapter. We clarified the situation by the formulation a few assumptions on EEG signals: the signal of interest is contained in the recorded time trace, the task-specific activations are a significant amount of the signal and cog-nitive processes are in some way stereotypical, such that there is a common part in the signal of similar trials. Based on these assumptions we show how more sophisticated analysis methods can be derived.

In order to robustly extract meaningful signals from noisy electrophysiological data, averaging over many similar trials is unavoidable. The nature of these data sets, i.e. correlations between electrodes, clustered time courses across trials and prior knowledge from the design of the experi-ment, suggests a number of more complex procedures for cleaning data and enhancing the quality of the signal.

We introduced our open source project, an algorithm toolboxlibeegtools providing mecha-nisms to enhance averaging of trials of a certain experimental condition. It is possible to reduce

5 Interlude: Advanced EEG Analysis

the variablity of the data by allowing for variable internal processing speeds. If two trials produce different reaction times, their processing must also have taken a different timecourse, thus a simple averaging does not comply with the situation. Our methods identify the relative temporal differ-ence in processing and uses the information in the averaging of both trials. The identification of the required time warping function can be done by an adaptation of the concept of recurrence plots.

As a side product we develop a metric which can successfully be used to cluster trials according to their timecourse of processing. Our algorithms can successfully be applied to artificial and real ERP-data significantly improving the quality of event related potentials compared to the traditional point-wise average. But the specific application to EEG data does not limit the generality of the approach which may as well be used for other imaging techniques.

6 Perception or Selection Effect

The third mainstay to temporally localize the negative priming effect are experimental paradigms that divide trial processing into several parts which can each be measured separately, as presented in the current and the following chapter. By assuring that the subtasks have to be accomplished in serial manner we can assign temporal differences in the processing of a certain experimental condition to specific parts of the trial.

The aim of the present study is to single out the stimulus identification phase from an experi-mental trial. The question whether negative priming is produced in the identification phase or in the phase of selecting the target over the distractor is believed to be crucial for the distinction be-tween memory based or representation based theories. The former assume a conflict in the target selection phase and the latter like Distractor Inhibition, see section 2.4.1, or the ISAM, chapter 3, assume interferences already in an early stage of a trial. We realize the trial splitting by showing a color cue indicating which of the two objects to attend after the two objects already disappeared.

Both objects have to be considered first, and afterwards the target has to be selected from memory.

We will describe the current experiment in section 6.1 which will also cover an introduction of the side effects to be expected when introducing a task switch as well as the extension of the set of experimental conditions. The hypotheses in section 6.3 are partly generated by a simulation of the ISAM. The necessary adaptations of the implementation are presented in section 6.2. To obtain comparability of the obtained results with previous studies, we run two preparatory experiments which are given in section 6.4. Section 6.5 is devoted to the presentation of the final post-cue experiments which will provide two individual reaction times for the two phases of each trial, stimulus identification and target selection. Finally all experiments of the study are discussed in connection in section 6.6.

6.1 Task Switch Paradigm

We realized the division of the trial processing by introducing a task switch dimension into the voicekey paradigm seen in section 2.2. The basic idea is that if the subject is given a color cue indicating the target after the presentation of the two stimulus objects, no information regarding which of the two shown objects is to be selected is available as long as the stimuli are visually present. Therefore, a complete identification of both objects is required before the trial can proceed by showing the color cue.

Additionally, the design brings along several difficulties. The most prominent one is the dimen-sion of task switching which is known to produce large behavioral effects (Monsell, 2003). We will discuss the impact of task switching to our paradigm in section 6.1.2 after the introduction of our series of experiments in the following section.