Product Details

Comprehensive Artifact Removal

Brain electrical activity is a weak biological signal that is easily interfered with by various non-brain signals (artifacts). The identification and removal of artifacts is crucial for accurate EEG interpretation, especially as long-term EEG monitoring technology becomes widely used in clinical practice, making it inevitable that EEG recordings contain various types of artifacts, posing challenges for EEG analysis.
Following clinical guidelines, EEG-X uses a default 0.53-70Hz filter configuration and employs multiple algorithms to identify artifacts from different sources, such as baseline drift, ECG, EMG signals, and motion-induced artifacts. After identifying these EEG signals, the system automatically removes related artifacts in the background while maximizing the preservation of the original EEG signal's integrity and accuracy, avoiding interference with genuine brain electrical signals.

Automated Multi-source Artifact Recognition & Removal

No manual intervention required - the algorithm automatically identifies and removes artifacts using appropriate methods based on their characteristics

Physiological Artifacts

ECG and Pulse
EMG Activity
Respiratory Movement

Instrument and Electrode Artifacts

Instrument Noise
Poor Electrode Contact
Incorrect Electrode Placement

Environmental Electromagnetic Interference

50Hz AC Interference
Static Electricity Interference

Motion-Induced Artifacts

Random Movement
Rhythmic Movement

Users can freely switch between displaying original EEG and artifact-removed EEG to adapt to different datasets and research needs

Overlay Display

Shows artifact-removed data as black solid lines in the EEG display area, with original EEG data as gray solid lines in the background

Artifact-Removed Only

Shows only artifact-removed data as black solid lines in the EEG display area

Original EEG Only

Shows only original EEG data as gray solid lines in the background

Enhanced Quantitative Statistical Analysis & Improved Spike/Wave Detection Performance

Artifact recognition and removal significantly improves the quality of EEG signal analysis. Many patients' EEG data contains strong artifact interference, making it difficult for clinicians to distinguish between changes caused by artifacts and actual brain activity. These artifacts not only mask real changes but may also produce numerous false positives, greatly reducing the accuracy of analysis results.
In contrast, comprehensive artifact removal allows quantitative statistical analysis results to primarily reflect changes in brain signals. After artifact removal, the spike/wave detection algorithm can more accurately extract the morphological features of brain waves, quantitatively describing various characteristic parameters such as duration, amplitude, and sharpness.