How did EEG pioneer the field of brain-computer interface (BCI)?

1 EEG: A window to the brain

EEG is a technique that measures the electrical signals generated by the neurons in the brain. EEG electrodes are attached to the scalp and record the fluctuations of the brain waves, which reflect different states of consciousness, cognition, and emotion. EEG was first discovered in 1875 by Richard Caton, who observed the electrical activity of animal brains. In 1924, Hans Berger recorded the first human EEG and identified the alpha and beta rhythms. EEG soon became a valuable tool for diagnosing and studying neurological disorders, such as epilepsy, coma, and brain death.

2 The first BCI experiments

The idea of using EEG to control a device or a feedback system was first explored in the 1960s and 1970s by several researchers, such as Jacques Vidal, Eberhard Fetz, and Jose Delgado. They demonstrated that humans and animals could learn to modulate their brain waves and use them to perform simple tasks, such as moving a cursor, switching a light, or stimulating a muscle. These experiments showed that EEG could be used as a source of information and control for BCI.

3 The evolution of BCI paradigms

Over the years, BCI research has developed different paradigms to classify and interpret EEG signals for a range of purposes. Event-related potentials (ERPs) are brain responses to specific stimuli, such as flashes, sounds, or words, and can be used to detect attention, recognition or intention. Steady-state evoked potentials (SSEPs), generated by repetitive stimuli like flickering lights or tones, can be used to create a virtual keyboard or binary switch. Sensorimotor rhythms (SMRs), brain waves related to body movement or imagination of movement, can control a cursor, robot arm or wheelchair. Lastly, slow cortical potentials (SCPs) are slow voltage shifts in the brain that reflect the level of arousal, expectation or inhibition and can be used to regulate attention, relaxation or pain.

4 The challenges and opportunities of BCI

Despite the advances in BCI research and technology, there are still many challenges and limitations to overcome, such as the low signal-to-noise ratio and variability of EEG signals, which require sophisticated algorithms and calibration to extract meaningful features and patterns, as well as the invasiveness and discomfort of EEG electrodes, which limit the usability and acceptability of BCI systems. There are also ethical and social implications of BCI, such as privacy, security, responsibility, and identity issues. On the other hand, there are many opportunities and benefits of BCI, such as enhancing human performance and creativity by augmenting cognitive, sensory, or motor functions or providing novel forms of expression and communication. Additionally, BCI can restore human abilities and quality of life by compensating for impairments or disabilities or facilitating rehabilitation and therapy. Moreover, it can explore human nature and potential by accessing and manipulating brain states and processes or creating new forms of experience and interaction. EEG was the first method of measuring brain activity for BCI and is still the most widely used; it has enabled the development of BCI as a field with many applications for human-machine interface.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?