Table 3 EEG during acute clinical assessment to identify radiological changes associated with large vessel occlusion (LVO)

Wang 2013 [39]

CT and/or MRI

110 Ischaemic Stroke patients (various lesion sizes)

Cognitive impairment; psychiatric disorders; traumatic brain injury; tumour; encephalitis; hydrocephalus; autoimmune disorders; brainstem stroke

 < 10

16 electrodes

Sampling 250 Hz, offline filter 0.5-50 Hz, computer, visual and EOG artifact removal, 2 s epochs,

Relative spectral power (beta only)

Larger infarct size associated with lower beta power (r₁ =  − 0.88881, P < 0.001)

3

Shreve 2019 [22]

CT and/or MRI

6 small infarct Ischaemic Stroke patients, 3 TIA patients, 5 large supratentorial infarct Ischaemic Stroke patients, 10 Stroke Mimic patients

Haemorrhagic stroke

 < 43.5

256 electrodes but 62 excluded, awake, fixed gaze with bed at 30 degree angle

Offline only sixth order < 50 Hz filter, independent component analysis artifact removal, 1 s epochs

Relative spectral power (All bands, global power, DAR, DTABR)

Compared to all other groups, large infarcts were associated with higher delta (p’s = 0.004–0.038) and DAR in both hemispheres (p’s = 0.0006–0.005), greater DTABR (p = 0.015) and lower beta (p = 0.04) in the contralesional hemisphere

4

Finnigan 2004 [12]

Only MRI (DWI) 15 h ± 3 h

11 Ischaemic Stroke patients (MCA; PCA; ICA)

Fever, encephalitis, seizures, ICH, non-cortical stroke, confounding neurological condition (e.g. previous stroke) or medication

 < 9

64 electrodes, between MRI scans

Online filter .01-100 Hz, artifacts 0.2- 40 Hz, automatic artifact removal, 4 s epochs, sampling 500 Hz, FFT .5-50 Hz

Relative spectral power (aDCI)

Larger infarct size associated with higher aDCI (rho = 0.62, P < 0.05)

3

Wolf 2016 [40]

Only MRI

69 Ischaemic Stroke patients

Seizure

 < 48

10–20 system

EEG abnormalities identified

Epileptiform activity (generalised or focal slowing or epileptiform potentials)

Abnormal EEG (p = 0.002) and focal EEG slowing (p = 0.013) associated with larger territorial infarcts (versus lacunar and embolic)

2

Erani 2020 [23]

Angiography (unclear modality)

43 Ischaemic Stroke patients (7 LVO), 7 Haemorrhagic Stroke patients, 13 TIA patients

Not Reported

 < 23

17 electrodes, portable, dry electrode system, eyes open, resting

Offline analysis (filtering and artifact removal) and re-referencing for bipolar montage

Relative spectral power (all bands, beta split into low and high)

Deep learning EEG (2 lasso selected electrode pairs) and clinical data model (AUC = 0.86, sensitivity = 76%, specificity = 80%) could identify stroke with LVO more accurately than combined clinical and EEG (2 electrode pairs) data (AUC = 0.78, sensitivity = 57%, specificity = 80%), and individual EEG (2 electrode pairs) (AUC = 0.69, sensitivity = 41%, specificity = .80%) or clinical (AUC = 0.80, sensitivity = 65%, specificity = 80%) data models.compared with all other stroke/TIA patients. Greater low frequencies (theta) and lower high frequencies (alpha) associated with LVO

4