EEG-data

Analyzing the EEG-data began with a visual inspection of the data to nd bad channels which were interpolated in BESA (MEGIS GmbH) using spline interpolation.

The data was then artifact-corrected using the "Surrogate method" (Berg &

Scherg 1994), rereferenced to average reference and projected on a source-montage with 8 sources using BESA (MEGIS GmbH). The source-source-montage used in this work was the same used in the diploma-thesis of Winfried Schlee (2006) [45]. An overview is provided in gure 2.3. For details see the ap-pendix. The three orientations of the source-projection were combined to one by calculating the maximum PCA-component of the three orientations.

Afterwards the data was exported to EEGLAB [12]. First, the data was bandpass-ltered. Cut-o frequencies diered whether event-related poten-tials or the aSSR was analyzed, so the lter-settings will be detailed in the sections describing the individual methods. Afterwards the data was grouped into epochs reaching from 1 second pre-stimulus to 5 seconds post-stimulus and the mean of the pre-stimulus interval was subtracted. Epochs were grouped by two factors the rst being the kind of feedback (High vs. Low) the second the number of consecutive trials with the same feedback.

Figure 2.3: An illustration of the source-montage used in this study. (After Schlee (2006) [45])

Event related Potentials

For ERP-analysis, the string lengths 24 were condensed to keep the number of trials on a level allowing for a proper ERP-analysis as high number of trials is needed to decrease the signal to noise ration. The data was bandpass ltered (HP: 1Hz, LP: 30Hz). Afterwards the mean of the groups for each subject was calculated and exported to R [36] for further analysis.

The amplitude and latency of the N100 was calculated because it was the most prominent component and observable in all subjects. The N100 was dened as the most negative sample in the time-range between 48 and 180ms.

A two-way ANOVA was performed on the amplitude and latency for each source using a linear-mixed-eect model. "Condition" and "Length" were used as xed factors while "Subject" was used as random-factor. "Condi-tion" means the kind of feedback (High or Low)."Length" refers to the string length.

Steady-State Analysis

Data was bandpass ltered (HP: 30Hz, LP: 50Hz) and separated into epochs.

Afterwards a moving-average was applied to each trial that works in the fol-lowing way: Each epoch was cut into numerous frames consisting of four cy-cles. Such a frame consisted of 25 data-points and was 100ms long. To elim-inate the eect of the transient response at stimulus-onset, the rst 750ms

of the data were discarded. The second frame started one cycle (25ms) after the rst, the third another cycle later and so on. The resulting 201 cycles were then averaged. This condensed the trial of six seconds into an averaged frame of 100ms.

A Fast-Fourier-Transform (FFT) was then computed on the condensed data to extract the amplitude and phase at the modulation-frequency.

Steady-State Amplitude First, the data was normalized in the following way: For each subject the mean amplitude over all conditions and sources was calculated. The amplitudes of each subject were then divided by the individual mean. The data was then exported to R [36] for statistical analysis.

The mean amplitude of each group was calculated and a three-way ANOVA was computed on the data again using a linear-mixed-eect model, using the factors "Source", "Condition", "Length" as xed eects and the factor

"Subject" as random eect. "Source" refers to the 8 sources of the source-montage. "Condition" and "Length" are dened according to the denition in the ERP-analysis section.

For a rened analysis, the 8 sources were analyzed separately this time using a two way (Condition * Length) ANOVA using the same method as stated above.

Source Coherence The Rayleigh Test of Uniformity was applied to the phase-dierences using PhasePACK package for Matlab (Rizutto 2004 [38]).

Therefore, the phase-dierences between the 8 sources for each condition were calculated. The mean-length served as test statistic. The null hypothesis of a random distribution around the circle was tested against the alternative hypothesis of a uniform distribution. In the case of a perfect uniform distri-bution, the mean resulting length equals one. The resulting data was then exported to R [36] for further statistical analysis.

Fisher's z-transform was applied to the data. Each combination of sources was analyzed separately with a three-way ANOVA using a linear-mixed-eect model statistic. The xed eects were "Condition" and "Length", the ran-dom eect "Subject". These were dened as in the analysis of the aSSR.

Chapter 3 Results

3.1 Behavioral Data

Figure 3.1 shows the results of the behavioral data. It shows that for a string length of one the subjects expected the same tone to follow on the next trial. For the string lengths two and three there is a trend that the subjects' expectation was at about 50% for either of the tones to follow. For the string length of four subjects expected the other tone to appear.

The results of the linear-mixed-eect-model Statistic are shown in table 3.1 The analysis revealed a signicant eect for the "String Length * Condition"

interaction. Neither of the main-eects was signicant.

0.00.20.40.60.81.0

Previous Position in String

Response

1 2 3 4

Condition Silence Noise Interaction with correction

Figure 3.1: Plot showing the behavioral Response depending on the String Length and Condition. Higher values on the y-axis mean the subject rated the current tone as the high tone.

numDF denDF F-value p-value

(Intercept) 1 88 259.60860 <.0001

String Length 1 88 0.02978 0.8634

Condition 1 88 0.77259 0.3818

String Length * Condition 1 88 5.48285 0.0215 Table 3.1: Linear Mixed-Eect-Model Statistic for the behavioral data.

Numerator degrees of freedom (numDF), denumerator degrees of freedom (denDF), F- and p-values are given for the full model.