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EEG (Electroencephalography)

Fully noninvasive scalp-based neural recording using surface electrodes, the oldest BCI approach with limited spatial resolution but zero surgical risk.

Fundamental Technology

Electroencephalography (EEG) records electrical brain activity through electrodes placed on the scalp, the oldest of the brain recording methods used in BCI, dating to Hans Berger’s first human recordings in 1924. EEG captures voltage fluctuations reflecting coordinated population neural activity across entire brain regions, recording from relatively distant neural sources through the intervening scalp, skull, and cerebrospinal fluid.

Advantages and Limitations

EEG is completely noninvasive with zero surgical risk, enabling research and application in any population without medical contraindication. However, spatial resolution is severely limited, with electrodes capturing blurred summations of activity from large brain volumes. Signal quality is substantially lower than intracortical or surface electrode approaches, limiting decoding accuracy.

Consumer and Research Applications

EEG dominates consumer BCI applications, including meditation apps, gaming interfaces, and attention monitoring systems. Research laboratories use EEG for large-scale population studies and longitudinal investigations spanning years or decades. In October 2026 Sabi raised a $50 million seed round led by Khosla Ventures for an EEG cap, and in August 2026 China’s NMPA approved Tiankai Suishi’s EEG cap for assessing cognitive impairment.

Signal Characteristics

EEG measures oscillatory rhythms in specific frequency bands including alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-100 Hz) activity. BCIs often decode intended movement or attention through modulation of sensorimotor rhythms; direct neural population decoding requires higher-resolution approaches.

Comparison to Invasive Methods

While EEG performs poorly compared to intracortical or endovascular approaches for communication BCIs, the complete absence of surgical risk makes EEG valuable for specific applications where perfect decoding accuracy is unnecessary. Recent advances in signal processing have marginally improved EEG BCI performance, though fundamental physical limitations persist.