Next Generation Internet: Brain-Machine Surfing, Human-Machine On-Chain 🧠

AI is currently thriving, yet there are not many breakthroughs on the technical front. Applications led by LLM interactive window robots are blooming, but the AI field has entered a stage of large-scale engineering and commercial expansion, while theoretically it has reached a stagnation bottleneck. Future assets and innovation hotspots will inevitably move towards brain-machine interfaces, alternative materials for new energy, and the space economy.

A Brain-Computer Interface (BCI) is a technology that enables direct interaction between the human brain and computers or other external devices by recording and decoding brain activity. Its core goal is to provide communication and control capabilities for patients with motor function disorders, while also extending to applications for healthy individuals (such as game control, attention monitoring, etc.).

Core Components of BCI:

🧠 Signal Acquisition

Invasive: Implanting electrodes through surgery (such as microelectrode arrays, ECoG), providing high signal quality but with infection risks.

Non-invasive: EEG (Electroencephalography): Records electrical activity through scalp electrodes, low cost but poor spatial resolution. MEG (Magnetoencephalography): Records magnetic field signals, high resolution but expensive equipment. fMRI (Functional Magnetic Resonance Imaging): Indirectly measures neural activity through blood oxygen level-dependent (BOLD) signals. fNIRS (Functional Near-Infrared Spectroscopy): Detects blood oxygen changes using light signals, portable but low temporal resolution.

🧠 Signal Types Event-Related Potentials (ERP): Such as P300 (a positive wave appearing after 300ms), used for spelling systems. Sensory Evoked Potentials: Such as Visual Evoked Potentials (VEP), Auditory Evoked Potentials (AEP). Sensorimotor Rhythm (SMR): Generated by imagining limb movements, used for controlling prosthetics or cursors.

🧠 Signal Processing Feature Extraction: Removing noise and extracting useful information, common methods include: Common Spatial Pattern (CSP): Maximizing the variance difference between two types of signals (see formula below). Independent Component Analysis (ICA): Separating signal sources and removing artifacts (such as blink interference). Wavelet Transform (WT): Extracting time-frequency features. Classification Algorithms: Mapping features to control commands, common methods include: Support Vector Machines (SVM): Separating different categories through hyperplanes. Neural Networks (NN): Such as Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN). Fuzzy Inference Systems (FIS): Handling uncertain signals.

Future Research Directions

1. Develop low-cost, high-resolution non-invasive devices (such as low-density EEG);

2. Combine high-performance deep learning algorithms (such as LSTM, Transformer) to improve classification accuracy.

3. Optimize real-time signal processing algorithms to reduce latency;

4. Expand application scenarios (such as emotion recognition, virtual reality control).