Week 9 Flashcards
Describe the characteristics of the five frequency bands (or waves) of the electroencephalography (EEG) signals that are prominent in certain states of mind
1) Delta waves (1-4 Hz), associated with unconscious mind
during deep dreamless sleep
2) Theta waves (4-7 Hz): associated with
subconscious mind, e.g., during sleep/dreams
3) Alpha waves (8-13 Hz)
associated to relaxed mental state more visible over parietal &
occipital lobes, high alpha activity correlates to brain inactivation
4) Beta waves (13-30Hz)
related to an active state of mind more prominent in frontal cortex &
over other areas during intense focused mental activity
5) Gamma waves (> 30 Hz), associated
with hyper brain activity
Physiological signals can be measured using
1) Electroencephalography (EEG):
- poor spatial resolution & requires many electrodes
- provides great time resolution ⇒ to study phase changes in response to emotional stimuli
- non-invasive, fast & inexpensive
- due to wearability, price, portability & ease-of-use of
wireless EEG devices
- ⇒ diverse applications:
entertainment, e-learning, virtual worlds, e-healthcare,
instant messaging, online games, assisting therapists &
psychologists
2) Functional Magnetic Resonance Imaging (fMRI)
3) Positron Emission Tomography (PET)
EEG distance betwen adjacent electrodes?
10%/20% of total
front-back/right-left distance of skull
EEG hemisphere location identifier?
1) z (zero) - electrode placed on mid line
2) even numbers - electrode
positions on right hemisphere
3) odd numbers - electrode
positions on left hemisphere
Anatomical landmarks for correct positioning of electrodes
1) nasion - point between forehead & nose
2) inion - lowest point of skull from back of head
3) pre auricular points anterior to the ear
Two types of electrodes EEG
1) monopolar - records potential difference, compared to a neutral electrode connected to an ear lobe or mastoid
2) bipolar - records potential difference between two paired electrodes
Noise sources EEG
1) muscle activity near active sites, eye movements & blinks
2) eye movement artifacts ⇒ profound effects on frontal brain sites, specifically mid-frontal sites
Which waves of electroencephalography (EEG) are related to emotions in the
brain?
1) right frontal activation - associated with withdrawal stimuli or
negative emotions, e.g., fear or disgust
2) left frontal activation - associated with approach stimuli or positive
emotions, e.g., joy or happiness, ⇒ asymmetrical frontal EEG activity - changes in valence
3) beta bands - related to valence
4) pre-frontal (& parietal) asymmetry in alpha & temporal asymmetry in gamma band - for arousal (&valence) recognition
5) Changes in gamma band are relate to happiness & sadness
6) Decrease in alpha wave in different sides of temporal lobe (left
for sadness, right for happiness)
Gender differences for EEG
1) men rely on recall of past emotional experiences to evaluate current
emotional experiences, have more individual differences among their EEG patterns
2) women, engage emotional system more readily, share more similar EEG patterns among them
Outline the steps involved in emotion recognition from EEG
1) Expose user to test stimulus
2) Record voltage changes in the brain
3) Remove noise & artifacts from recorded signals
4) Analyse resulting data & extract features
5) Train classifier & use computed features ⇒ interpret raw signals
What are the main approaches to removing artefacts from EEG recording?
1) providing
information to participants about their posture
2) manually remove data due to different types of artifacts associated to participants
3) Blind Source Separation (BSS) & Independent Component Analysis (ICA): for removing artefacts due to eye movements, blinks & line noise
4) other methods: Common Average Reference (CAR),
Laplacian, or Average Mean Reference (AMR)
Test protocols for EEG
1) Subjects:
- sufficient number to verify accuracy & meaningfulness of data/results
- balanced number of subjects from each gender
2) Approaches to emotion elicitation:
- subject-elicited, ask participants to remember past emotional episodes of
their life or act as if they were feeling a given emotion
- event-elicited, use different modalities: visual, auditory, tactile or odour stimulation, cover desired arousal levels & valence states
3) influenced by complexity & number of targeted emotions
4) Ground truth of emotional state:
- self-ratings of subjects
use standard stimulus sets,
- duration of an affective phenomenon ranges from full blown emotions (e.g. minutes) to traits, lasting for years if not a lifetime
Sampling freqs for EEG
512, 256, 1024, 2048, 128, 500 Hz
What considerations should be made when selecting EEG features and electrodes?
1) assumptions:
- spectral power in various frequency bands is often
associated to emotional states
- coherence & phase synchronization of pairs of electrodes
- frontal asymmetry in a band power as differentiator of valence levels
2) Most appropriate features not agreed upon
3) features in time, frequency, time-freq domain
4) calculated from recorded signal of single electrode
What does normalised 1st difference feature measure?
captures self-similarities of EEG signal
What do Hjorth features measure?
activity, mobility, complexity
What does non-stationary index (NSI) measure?
complexity by analysing variation of local average over time
What does fractal dimension measure?
measures complexity, produces results closer to theoretical FD than other
methods
What do high order crossing features measure?
1) captures oscillatory pattern of EEG signal
2) applies a sequence of high-pass filters to zero-mean time series Z(t)
Which frequency bands are used to extract power features?
1) 1-4 Hz, delta
2) 4-8, theta
3) 8-10, slow alpha
4) 8-12, alpha
5) 12-30, beta
6) 30-64, gamma
What features can be extracted from the discrete wavelet transform of an EEG
signal?
1) decomposes signal in different approximation & detail levels corresponding to different frequency ranges,
conserves time information of signal
2) ⇐ correspondence of frequency bands & wavelet
decomposition levels depends on sampling frequency
3) features: energy corresponding to the bands
frequencies of α, β and γ, via root mean square
(RMS), recursive energy efficiency
How can features from combinations of electrodes be measured?
1) Magnitude Squared Coherence Estimate (MSCE)
2) hemispherical asymmetry:
- differential, differences in power bands of pairs of electrodes
- rational, use spectrogram to compute power ratios for consecutive time windows
Two types of feature selection methods
1) wrapper methods - iteratively select features based on classifier performance, computationally expensive
2) Filter methods - use measure of info (e.g. variance), select features with variance above threshold, less computational power but ignores cross-feature relationships
Describe the process of feature selection in ReliefF
1) univariate technique
2) uses subsample of instances to adjust weights of each feature depending on ability to discriminate between two classes
3) estimates weight quality for each feature based on difference to nearest hit/miss
4) to account for multiple classes, search n nearest instances from each class weighted by prior probabilitites