Decoding Chess Puzzle Play and Standard Cognitive Tasks for BCI: A Low-Cost EEG Study

2025-05-12

AI Summary

This study investigates the feasibility of using low-cost, consumer-grade EEG devices for recognizing multi-level cognitive load and diverse task types in brain-computer interface (BCI) applications. We employed the MUSE 2 dry-electrode headset and designed a novel experimental paradigm integrating canonical cognitive tasks—N-Back, Stroop, and mental rotation—with ecologically valid chess puzzles, marking the first standardized adoption of chess as a cognitive load paradigm in EEG-BCI research. Using machine learning classifiers and cross-task transfer modeling, we achieved: (1) statistically significant discrimination among N-Back load levels (accuracy substantially exceeding chance); (2) high-accuracy classification across distinct task types; and (3) empirical validation that consumer-grade EEG hardware can support real-time, adaptive BCI systems. All code and datasets are publicly released to foster reproducibility and community advancement.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

While consumer-grade electroencephalography (EEG) devices show promise for Brain-Computer Interface (BCI) applications, their efficacy in detecting subtle cognitive states remains understudied. Using a combination of established cognitive paradigms (N-Back, Stroop, and Mental Rotation) and a novel ecological task (Chess puzzles), we demonstrate successful distinctions of workload levels within some tasks, as well as differentiation between task types using the MUSE 2 device. With machine learning we further show reliable predictive power to differentiate between workload levels in the N-Back task, while also achieving effective cross-task classification. These findings demonstrate that consumer-grade EEG devices can effectively detect and differentiate various forms of cognitive workload, and that they can be leveraged with some success towards real-time classification distinguishing workload in some tasks, as well as in differentiating between nuanced cognitive states, supporting their potential use in adaptive BCI applications. Research code and data are further provided for future researchers.

Problem

• Detecting cognitive states with low-cost EEG devices
• Differentiating workload levels in cognitive tasks
• Enabling real-time classification for adaptive BCI applications

Innovation

• Used consumer-grade EEG for cognitive state detection
• Combined traditional and novel ecological tasks
• Applied machine learning for workload classification