The Science Behind Brain-Computer Interfaces
Brain-computer interfaces (BCI) offer a direct channel between your thoughts and computer output, both visible on your scalp or implanted within brain tissue.
These devices enable people who do not use their hands to use things such as controlling computer cursors or communicating with virtual assistants, as well as restore motor functions for those paralyzed due to stroke or severe injury.
Electroencephalography (EEG)
Electrodes placed on the scalp can measure electrical activity of dendrites of brain dendrites, with frequencies associated with different mental states. Electroencephalography (EEG), first discovered by Hans Berger in 1924, has long been used medically as a powerful tool to measure brainwave patterns and translate them into external actuators.
EEGs capture rhythmic neuronal activity as well as transient events like spikes and waves which indicate interictal or seizure activity or pre-ictal activity in those living with epilepsy. Furthermore, normal features like vertex waves and sleep spindles may also be visible on an EEG scan.
An EEG requires patients to relax and perform various tasks such as breathing deeply, reading aloud and looking at flashing lights. An EEG record can then be collected using electrodes on their scalps; alternatively it may be combined with other techniques such as fMRI and fUS to capture more detailed temporal resolution data.
Data collected via EEG can be processed into a model of brain dynamics and neural processes using various techniques such as spectral analysis implemented through free software such as EEGLAB. Once complete, this model can then be used to detect patterns which suggest pathological conditions as well as provide insight into someone's cognitive status.
Neurofeedback (NF)
The theory behind neurofeedback (NF) is that individuals can learn to manage their brainwaves through feedback loops. It employs similar general principles as other biofeedback interventions such as cardiac electrophysiology and tai chi, yet specializes on EEG parameters specifically.
Basic Neurofeedback protocols utilise the bulldozer principle (see Fig 4.2.6) which involves targeting abnormal EEG patterns through biofeedback training to target and reduce them. If too much activity exists within one frequency band this should be trained down, while too little activity must be increased through training in another frequency band.
These neurofeedback techniques can be utilized to improve performance among athletes, musicians, students, workers and workers as well as treat organic brain conditions such as seizures, autism spectrum disorders or migraines.
Neurofeedback uses technology that monitors and translates fluctuations in physical brain activity into visual displays on screens (e.g., computer monitor). Subjects should attempt to use the display's cues up or down for activation/deactivation by visual or auditory means in order to alter their brainwaves for greater efficiency through voluntary efforts to shift them toward more efficient modes. Although many studies have reported positive findings about NF, more work needs to be done on understanding its efficacy. Various reports have documented its success but given small participant numbers involved as well as research design shortcomings a significant amount of work remains for understanding its efficacy fully.
Neural Connectivity
Neurons connect through axons that transmit signals between different brain regions. These connections, known as neural pathways or fasciculi, include shorter axon segments within grey matter as well as longer projections (known as white matter) myelinated by the nervous system. Connectivity among regions may either be structural or functional: Structural connectivity refers to direct connections between neurons while functional connectivity refers to how an area impacts another one of their activities.
Studying neural connectivity involves various analysis approaches, including graph theoretical and community detection methods, as well as morphometric techniques that quantify neuronal structures and their connections. Neuronal structure and function, along with the related connectivity patterns, are highly specific yet variable; specific neurons exist within distinct cortical columns or minicolumns and coordinated activation of distributed populations is responsible for producing coherent cognitive states.
Estimating functionally relevant connectivity using a multivariate autoregressive model and artificial neural network, known as nCREANN, allows us to accurately identify both linear and nonlinear causal relations between two brain regions. Linear components relate directly to input/output relationships while nonlinear ones reflect influences between an input and subsequent outputs.
Machine Learning
Machine learning usage in brain-computer interfaces (BCI) has proven essential as it enables these systems to interpret and interpret complex brain signals collected during brain signal analysis in order to execute commands effectively.
BCIs hold tremendous potential to revolutionize medical fields and health care; they can also be applied in other areas like entertainment/games, self-control, security and marketing. It should be noted, however, that BCI technologies are still in their experimental stage and must be treated as such.
Non-invasive brain-computer interfaces, usually worn as headsets or caps, measure electrical activity within the brain and convert it to a digital signal that can be read by software. Once recognized by software, this signal conveys user intent for actions like turning on lights (for instance).
Non-invasive approaches have proven successful at helping patients recover from neuromuscular disorders, yet researchers continue to refine its accuracy. They are exploring methods of dealing with natural physiology of the brain causing changes to signals; also cost reduction so the technology becomes mainstream; for example if BCIs can replace speech devices they could cost as little as $500 but that does not account for ongoing maintenance costs or health checks.
