Classical method

CSP+LDA: results on public EEG datasets

CSP+LDA is a classical two-stage pipeline for motor-imagery EEG: common spatial patterns (CSP) learns spatial filters whose output variances differ most between two classes, and linear discriminant analysis (LDA) classifies the log-variances of the filtered signals. Ramoser, Müller-Gerking and Pfurtscheller (2000) applied CSP with log-variance features and a linear classifier to imagined left- and right-hand movement.

Also known as CSP+LDA · Common Spatial Patterns · CSP · Ramoser et al. 2000

Reference Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 8(4):441–446, 2000. ↗

Directory status: Evaluated · Implementation used here ↗ · Description sources pubmed.ncbi.nlm.nih.gov · doi.org · mne.tools

Published results

Grouped by dataset. Compare figures within a group only: across groups the task, cohort, chance level and electrode layout all change.

ds003810 · Core matrix · motor imagery & rest

Read with its protocol: Core matrix (home page) · Chance level 50.0% · experiments.json

Method	Condition	Metric	Value	People
CSP+LDA	Rest versus right-hand imagery	Balanced accuracy	47.1%43.8%–50.2%	10

ds005342 · Core matrix · idle & command

Read with its protocol: Core matrix (home page) · experiments.json

Method	Condition	Metric	Value	People
CSP+LDA	Seated motor imagery · cue-gated replay	Command detection ≤3 s	23.3%	4
CSP+LDA	Seated motor imagery · cue-gated replay	Idle false activation	0.0%	4

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