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

AI is currently in full swing; however, there are not many breakthroughs at the technical level. Applications led by LLM interactive window robots are flourishing, but the AI field has entered a stage of large-scale engineering and commercial expansion, and it has reached a stagnation bottleneck at the theoretical level. Future assets and innovation hotspots will inevitably move towards brain-machine interfaces, new energy substitute materials, and the space economy.

Core components of BCI:

🧠Signal Acquisition
Invasive: Implanting electrodes (such as microelectrode arrays, ECoG) through surgery, high signal quality but with a risk of infection.
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 changes in blood oxygen using light signals, portable but low temporal resolution.

🧠Signal Types Event-Related Potentials (ERP): such as P300 (positive wave appearing 300ms later), 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 to control prosthetics or cursors.

🧠Signal Processing Feature Extraction: Remove noise and extract 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 Machine (SVM): Separating different categories through hyperplanes. Neural Networks (NN): Such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). Fuzzy Inference System (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).