Brain Machine Interface

Brain-machine interfaces (BMIs) aim to restore lost function by directly translating brain activity into control signals for external devices. Current research heavily emphasizes improving the efficiency and accuracy of BMIs through advancements in neural decoding algorithms, including recurrent spiking neural networks (RSNNs) and hybrid architectures combining convolutional and spiking networks, often incorporating self-supervised pre-training techniques to reduce reliance on large labeled datasets. These improvements are crucial for creating more compact, power-efficient, and reliable implantable BMIs for applications ranging from restoring motor control in paralyzed individuals to enabling communication for those with speech impairments.