Progress and Challenges in Physiological Artifacts’ Detection in ElectroencephalographicReadings
Bibliographic record
Abstract
BACKGROUND: Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time. INTRODUCTION: During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling. METHODS: This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing. RESULTS: Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue. CONCLUSION: Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert's burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".