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Created page with "RATIONALE Pain is subjective, which can lead to a host of issues (under/over reporting, malinguering, non-verbal, cognitive impairment, powering research studies) BACKGROUND..."
RATIONALE
Pain is subjective, which can lead to a host of issues (under/over reporting, malinguering, non-verbal, cognitive impairment, powering research studies)
BACKGROUND
EEG basics--neuronal firing, field potentials, signal processing (downsampling, Nyquist sampling theory, band-pass filters, FFT, artifact removal), power bands (delta, theta, alpha, beta, gamma), spectral analysis (BIS), sources of artifact
Gamma frequency has been shown to correlate with sensory stimulus/pain intensity, but is also the primary band for muscle artifact (10-200 fold decrease in gamma after paralysis), thus potentially contaminating prior data.
Pain protocols typically are either tonic (thermal) or phasic (laser pulse).
METHODS
RESULTS
DISCUSSION
Questions:
Infomax is an ICA algorithm that iteratively adjusts the mixing matrix to maximize mutual information via gradient ascent optimization.
Mutual information--the amount of information that can be obtained about one random variable through another random variable.
Mixing matrix--
Pain is subjective, which can lead to a host of issues (under/over reporting, malinguering, non-verbal, cognitive impairment, powering research studies)
BACKGROUND
EEG basics--neuronal firing, field potentials, signal processing (downsampling, Nyquist sampling theory, band-pass filters, FFT, artifact removal), power bands (delta, theta, alpha, beta, gamma), spectral analysis (BIS), sources of artifact
Gamma frequency has been shown to correlate with sensory stimulus/pain intensity, but is also the primary band for muscle artifact (10-200 fold decrease in gamma after paralysis), thus potentially contaminating prior data.
Pain protocols typically are either tonic (thermal) or phasic (laser pulse).
METHODS
RESULTS
DISCUSSION
Questions:
Infomax is an ICA algorithm that iteratively adjusts the mixing matrix to maximize mutual information via gradient ascent optimization.
Mutual information--the amount of information that can be obtained about one random variable through another random variable.
Mixing matrix--