Neuro-adaptive Artificial Intelligence (AI) trading systems represent an emerging interdisciplinary field that combines neuroscience, behavioral finance, and automated financial decision-making. This study provides a systematic literature review examining how insights from brain-computer interfaces (BCIs), affective computing, neuroeconomics, and reinforcement learning can be applied to improve algorithmic trading performance. Using a PRISMA-guided screening process across major academic databases, relevant peer-reviewed studies were analyzed to identify prevailing research methods, neural signal acquisition technologies such as electroencephalography (EEG) and physiological sensors, feature extraction techniques, and integration strategies for incorporating neurophysiological data into AI-based trading models.The review identifies three prominent research directions within neuro-adaptive trading: cognitive-state-based risk management, human-in-the-loop adaptive trading systems, and neuro-informed autonomous trading architectures. Findings from the literature indicate that neurophysiological indicators including stress responses, attention fluctuations, and cognitive workload can provide valuable information for reducing behavioral biases, improving risk-adjusted trading outcomes, and enhancing decision stability in volatile markets. Despite these opportunities, several challenges remain, including concerns about privacy and ethical use of neural data, reliability issues associated with noisy brain signals, risks of model overfitting, and limited regulatory frameworks. This review proposes a conceptual foundation for neuro-adaptive trading pipelines and highlights future research directions involving multimodal sensing, explainable AI integration, and responsible governance of neuro-financial technologies.
Neuro-adaptive AI; Algorithmic trading; Brain-computer interfaces; Behavioral finance; Reinforcement learning.
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