EEG and Behavioral Data for an N-Back Working Memory Task

Preprocessed EEG and behavioral data from a working memory task, specifically 0-, 1-, and 2-back conditions. The dataset was created by Johan Nakuci of the Georgia Institute of Technology for the associated research paper. The specific number of participants, trials, and recording duration is not detailed in the provided metadata.

Use Cases

• Analyzing single-trial neural variability based on the preprocessed EEG signals described.
• Modeling the relationship between behavioral performance and brain activity using the combined EEG and response data.
• Investigating distinct cognitive processing sequences during working memory tasks as suggested by the paper's title.
• Benchmarking machine learning models for EEG classification across different n-back difficulty levels.

Strengths

• Data is preprocessed, which may reduce initial processing burden for researchers.
• Includes both neural (EEG) and behavioral data, enabling multimodal analysis.
• Covers multiple task conditions (0-, 1-, and 2-back) to study varying cognitive load.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and data size are unknown, which may limit suitability assessment.

Provenance

Source

Johan Nakuci, Georgia Institute of Technology

Collection Method

Collected during a controlled working memory (n-back) task experiment.