administered during which a picture was presented showing the hands in the starting posture and the central red button illuminated to demarcate the pause interval from the sequence repetitions. In the imitation task subjects were asked to replicate the observed sequence of movements after having seen 4 sequence repetitions, using the same response box that was depicted on the computer screen. Subjects first pushed the two buttons that represented the starting positions. As a result, the central button on the response pad was illuminated for 5000 ms, during which a text was presented on the screen informing the subject that, as soon as the centre button on the response pad was turned off, they had to execute the observed movement sequence as accurately as possible. Accuracy of each movement was evaluated with respect to whether subjects released the correct starting button (i.e. whether they used the correct hand) and whether they pushed the correct target button. If either the wrong hand was used or the wrong target button was pushed in one of the movements, subjects received feedback that they made one or more errors. If the sequence of movements was executed correctly, the time to execute the entire sequence was presented on the screen. Subjects pushed a button on the button pad when they were ready to start a new sequence. In the detection task, the movements and sequences were the same as in the imitation task with the exception that in 20% of the presented sequences one of the movements was replaced by a catch movement in which a target button was pushed with the thumb instead of the index finger. After each presentation of the sequence, subjects had to report if a catch movement had occurred. If so, they had to push any button on the response-box within the 5000 ms during which the visual pause was adminis- tered. If the subject missed a catch movement or responded falsely, i.e. when no deviant was shown, ‘‘WRONG!’’ was projected over the warning display for 1000 ms, after which a new, neutral warning signal was displayed for 5 s, and the experiment continued. Sequences containing catch movements or false alarms were omitted from the EEG analyses to rule out the possibility of response preparation contaminating the detection task. Electrophysiological recording Sixty-one-channel EEG was recorded according to the International 10–20 System of Electrode Placement [47], using a BrainVision QuickAmp amplifier (Brain Products GmbH, Mu- nich, Germany) in combination with shielded Ag/AgCl electrodes. Electrode impedances were kept below 10 kV. EEG was sampled at 500 Hz with a 140 Hz low pass filter and a 50 Hz notch filter. An average reference was used during acquisition of the EEG signal. Because arm and hand movements are known to influence the mu rhythm, participants were instructed to position their hands along each side of the response box on the table and to move as little as possible during sequence observation in both tasks. Electromyograms (EMG) were measured using bipolar electrodes over flexor carpi radialis and extensor pollicis longus on both forearms, so that covert movements made by participants could be detected [17]. Vertical and horizontal electro-oculograms (EOG) were recorded using bipolar electrodes placed at the supraorbital and infraorbital ridge of the right eye and the outer canthi of the left and right eye respectively. Participants were instructed to fixate on the middle button of the response box presented on the computer monitor during sequence observation in both tasks. EEG, EMG and EOG data were analyzed offline using BrainVision Analyzer v1.05 (Brain Products GmbH, Munich, Germany) and BESA 5.1.6. (Megis Software GmbH, Gra¨felfing, Germany). Observational Learning Analysis Power analysis. EEG recordings in the imitation and detection tasks were analyzed separately for observation intervals and pause intervals to distinguish between activation generated by the observation and retention of movements. An additional analysis was directed at the execution interval of the imitation task during which participants performed the movement sequence from memory. Observation intervals (6000 ms interval consisting of 6 consecutive button presses of 1 s each), pause segments (5000 ms interval following each sequence repetition), and the execution interval (movement onset until movement end), were segmented into equally sized epochs (of 512 time points). Epochs containing EEG artefacts or EMG activity (execution interval excluded) were discarded from analysis using an automated procedure. This resulted in a rejection of 15.5% of all epochs in the detection task and 8.8% of all epochs in the imitation task. Furthermore, segments in which eye blinks were detected were discarded, resulting in an additional rejection of 10.4% of all trials of the detection task and 14.9% of the imitation task. For each individual subject, Fast Fourier Transforms (FFT) were performed on the artefact free EEG segments (512 points, Hanning window, 10% envelope), and averaged to create separate power frequency spectra for the observation and pause intervals per repetition in the imitation and detection tasks. Each participant’s individual mu rhythm band was determined by subtracting the power frequency spectrum of the sequence execution phase during the imitation task (which contained the lowest mu activation due to hand movements) from the power frequency spectrum during the observation phase of the detection task (which contained the highest mu activation). For each participant this difference was topographically mapped and the location of maximal mu modulation identified to determine each participant’s individual mu frequency [48]. Mu power was calculated using a +/20.5 Hz frequency bandwidth around each individual’s mu frequency. Similarly, for each participant the individual alpha rhythm was determined by subtracting the frequency spectrum in the condition with the lowest alpha power (observation of movements in the imitation task) from the condition with the highest power of alpha (pause in the detection task). For each individual, the posterior location and the frequency at which alpha power was maximal were identified. Alpha power was calculated using a +/20.5 Hz bandwidth around the each individual’s alpha frequency. For statistical analysis bilateral electrodes were identified at which modulations in mu power (C3 and C4) and alpha power (PO3 and PO4) were found maximal over subjects. Data from left and right hemispheres were pooled in the statistical analyses. Source localization. Localization of the effects in alpha and mu power (pause-observation) was established using minimum- norm analysis in BESA 5.1.6. (Brain Electrical Source Analysis, MEGIS Software, Germany). The minimal norm was computed with spatiotemporal weighting according to [49] fitted to an individual Talairach transformed brain surface. Coherence analysis. Raw data were filtered off-line (1– 50 Hz, slope 12 dB/octave) in advance of coherence analysis. Epochs containing artefacts were discarded from analysis using an automatic procedure. Segments with eye blinks were rejected. Before coherence analysis a current source density analysis was conducted (order of splines = 4, 10 polynomials) to remove spreading of nearby source activation to neighbouring electrodes [50,51]. An FFT was performed on each segment before calculating the coherences within each segment. Coherence values between electrode pairs were calculated for the frequencies within the 8–13 Hz frequency range. Similar to the PLoS ONE | www.plosone.org 9 December 2010 | Volume 5 | Issue 12 | e14482