EEG, EMG, and IMU Data for a BCI Aiming to Suppress Neurological Tremor

Giuliana Grimaldi provides a dataset of EEG, EMG, accelerometer, and gyroscope data for a patient with neurological tremor. The data is intended for a Functional Electrical Stimulation (FES)-mediated brain-computer interface. It includes 15 EEG channels, 15-channel EMG data from multiple muscles, and 3-axis accelerometer and gyroscope data, all sampled at 1000Hz.

Use Cases

• Computing cortico-muscular coherence based on the described EEG-EMG data coupling.
• Developing tremor suppression algorithms based on the multi-modal EEG, EMG, and inertial measurement unit (IMU) signals.
• Analyzing kinematic patterns of tremor using the 1000Hz accelerometer and gyroscope data.
• Validating quality parameters for BCI systems based on the provided QPs and QPsq threshold data.

Strengths

• Multi-modal data includes EEG, EMG, accelerometer, and gyroscope signals, enabling comprehensive analysis.
• High temporal resolution with a 1000Hz sampling rate for all signal types.
• Data is from a specific patient case, providing a focused clinical context.

Limitations

• Row count and overall dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description notes an initial silent period in the gyroscope data, which may require preprocessing.

Provenance

Source

Giuliana Grimaldi via paperswithcode

Collection Method

Likely collected from a patient undergoing BCI/FES experimentation.