Electroencephalography (EEG) BCIs represent the most widely used and accessible form of non-invasive brain-computer interface technology. EEG-based BCIs detect electrical activity on the scalp surface through electrodes, enabling direct communication between the brain and external devices without surgical implantation. [1]
EEG BCIs have become the foundation for numerous clinical and consumer applications, particularly in neurorehabilitation, assistive communication, and cognitive monitoring for neurodegenerative diseases. [2]
EEG signals are generated by the synchronized electrical activity of millions of neurons in the cerebral cortex. The signals are measured in microvolts (μV) and typically fall within the 0.5-100 Hz frequency range. Modern EEG systems use: [3]
EEG signals are categorized into frequency bands, each associated with different cognitive states: [4]
| Band | Frequency | Associated State | [5]
|------|-----------|------------------|
| Delta | 0.5-4 Hz | Deep sleep, unconscious |
| Theta | 4-8 Hz | Drowsiness, meditation |
| Alpha | 8-13 Hz | Relaxation, eyes closed |
| Beta | 13-30 Hz | Active thinking, focus |
| Gamma | 30-100 Hz | High-level cognition |
Motor imagery BCIs detect imagined movements from sensorimotor cortex activity. Users mentally simulate movements (e.g., moving a hand) without physical motion, producing detectable changes in mu (8-12 Hz) and beta (13-30 Hz) rhythms.
P300 BCIs detect the "oddball" response—a positive brainwave that occurs ~300ms after a rare or target stimulus. By presenting a matrix of symbols and detecting which item the user focuses on, communication becomes possible.
SSVEP BCIs use flickering visual stimuli at specific frequencies (typically 6-30 Hz). The brain's steady-state response to these stimuli produces enhanced activity at the stimulation frequency.
SCP BCIs train users to self-regulate slow voltage shifts in the EEG, associated with movement preparation and cognitive processes.
EEG BCIs provide augmentative communication for patients with locked-in syndrome, allowing text entry through P300 or motor imagery paradigms.
Motor imagery BCIs combined with functional electrical stimulation (FES) promote neuroplasticity and motor recovery. The brain is entrained to re-establish neural pathways for movement.
EEG-based neurofeedback targets mu and beta rhythms to reduce motor symptoms. Closed-loop systems can detect movement intention and provide adaptive stimulation.
EEG biomarkers serve for early detection and cognitive monitoring. Entrainment protocols may improve memory function through gamma-frequency stimulation.
| System | Channels | Type | Applications |
|---|---|---|---|
| OpenBCI Cyton | 8-32 | Dry/Wet | Research, hobbyists |
| Muse S | 4 | Dry | Meditation, sleep |
| Emotiv EPOC+ | 14 | Wet | Research, gaming |
| g.tec g.RECORD | 64+ | Wet | Clinical research |
| Kernel Flow | 64 | Dry | Research |
Wolpaw et al. Brain-computer interfaces for communication and control (2002). 2002. ↩︎
McFarland et al. EEG-based brain-computer interface (2010). 2010. ↩︎
Ramos-Murguialday et al. Brain-machine interface in chronic stroke rehabilitation (2013). 2013. ↩︎
Pfurtscheller et al. Motor imagery and EEG (2000). 2000. ↩︎
Krusienski et al. The P300 Speller (2006). 2006. ↩︎