Cognitive Neural Prosthetics

Annu Rev Psychol, 2010 · DOI: 10.1146/annurev.psych.093008.100503 · Published: January 1, 2010

Simple Explanation

Cognitive Neural Prosthetics (CNPs) assist paralyzed or amputee patients by recording the cognitive state instead of motor execution or sensation signals. CNPs extract cognitive functions, using the specialization of different cortical areas to their advantage, for example, decoding speech directly from speech areas for mute patients. CNPs can use parallel decoding, placing implants in multiple cortical areas to decode different signals simultaneously, beneficial for complex, multi-effector movements.

Study Duration

Not specified

Participants

Healthy monkeys and human patients

Evidence Level

Review

Key Findings

1
Intended goals can be decoded from prefrontal and parietal regions, allowing for rapid applications like 'typing' using letter boards.
2
Decision variables, including the amount, type, and probability of reward, can be decoded from parietal cortex.
3
Local field potentials (LFPs) are superior to spikes for decoding cognitive state in parietal cortex.

Research Summary

Cognitive neural prosthetics (CNPs) tap into higher brain functions beyond motor commands or sensory signals, encompassing intention, planning, attention, decision-making, executive control, emotions, learning, and speech. CNPs can decode executive functions for sensorimotor tasks and potentially for categorization, spatial attention, and abstract thought. Decoding algorithms used in brain-control experiments share parallels with brain function, suggesting that they may be successful due to these similarities.

Practical Implications

Enhanced Communication

Goal decoding can be used for rapid applications such as 'typing' using letter boards, enabling faster communication for disabled individuals.

Mood and Preference Detection

Decoding reward expectation can help in registering the preferences and mood of patients, allowing for more personalized care.

Improved Prosthetic Control

Using forward models derived from efference copy signals can generate trajectories for CNPs, even without actual movement, improving prosthetic control.

Study Limitations

1
Limited understanding of which brain areas show more plasticity for particular tasks.
2
Future challenge to extend CNPs to multiple cortical areas.
3
Currently based primarily on spiking activity; further research needed into other signal sources.

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