EEG and Eye-Tracking Preprocessing Steps

• preprocessed by MATLAB
• triggers and latencies extracted and specific temporal order of patients determined 
• replace and identify bad electrodes (electrodes that are > 3 stdev away from other electrodes in frequency spectrum distribution) using spherical spline interpolation (?)
• noisy channels filtered - either visually or replaced entirely by zeros
• 109 channels for EEG,  6 EOG for artifact removal

More Preprocessing

• data high pass filtered at 0.1 Hz and notch filtered at 59-61 Hz
• Principal Components Analysis (PCA) algorithm removed sparse noise - recovers a low-rank matrix from a corrupted matrix by subtracting the error.
• visual inspection after preprocessing as well.

Tools for EEG Analysis

• EYE-EEG: plugin for MATLAB- integrates analysis for electrophysiological + eye-tracking data.
• adds eye-tracking data as extra channels to EEG
• Adaptive velocity-based algorithm to detect saccades and fixations, i.e. a blink
  • identifies fixation as groups of consecutive points
  • uses that to map minimum and maximum x and y values - compare that to maximum dispersion value.
  • if the dispersion is lower, then its a fixation. 

Sequence Learning Paradigm

• EEG data segmented based on stimulus of each filled white circles at each of the eight different locations
• 900 ms long epochs (100 ms pre, 800 ms post)
• averaged trial types (hits, missed, learned) for each subject individually and calculated group average
• found a decrease of P300 over different blocks
• behavioral performance: percentage of correctly learned spatial location
• learning rate: newly learned location in relation to all possible locations

More Sequence Learning Paradigm

• P300 amplitude = amplitude on electrode CPZ, with latency of 350-500 ms post stimulus
• slightly delayed because children have greater latency
• performance increases while learning rate and P300 decrease w practice
• recall the expectation that subjects learn to expect the order of the appearance of targets w/ 5 blocks
• decrease in P300 because of novelty indicator of a target stimulus- subjects are not surprised anymore!

Surround Suppression Paradigm

• extracted based on flickering stimulus
• 4 foreground contrast (0%, 30%, 60%, 100%) and 3 background conditions (parallel, orthogonal, none)
• data of two blocks merged for each subject
• computed SSVEP signal without background, and a SSVEP averaged all conditions with a background
• FFT computed to obtain a measure of SSVEP power at 25 Hz freq
• subtracted this from neighboring freq to get the actual evoked activity

More on Surround Suppression Paradigm

• in-house algorithm to detect highest SSVEP amplitude, compute signal based on the average of max electrode + four surrounding electrodes
• graph to demonstrate the difference in SSVEP amplitude between different contrasts with or without a background
• increase SSVEP amplitude with an increase in foreground contrast
• relative reduction in SSVEP amplitude due to surround contrast

Contrast Change Detection Paradigm

• data of three blocks merged for each subject
• target epochs extracted from 500 ms before target to 1000 ms after peak sensory evidence
• response-locked epochs extracted from 1000 ms before repsonse to 300 ms after response (buttons)
• rejected trials for scalp channel over 100 microV
• SSVEP based on stimulus locked epochs + motor response signal based on response-locked epochs measured by Fourier
• CPP analysis averages single-trial  waveforms (baseline corrected 500 ms before response)

More on Contrast Change Detection Paradigm

• Figure to show SSVEP, CPP, and motor response signal topographies
• Result shows posterior maximum for SSVEP around electrode Oz. 
• CPP shows highest activity near CPz electrode and motor response signal peaks over C3 and C4.