Recent achievements demonstrate that it is currently possible to implement crude brain-computer interfaces (brain dishes) that allow in vitro neuronal clusters to directly control computers. The researcher Miguel Nicolelis has also used the vector sum of motor cortical neuron spiking – recorded directly from the cortex of monkeys – as a BCI. This design allowed a monkey to navigate a computer cursor on screen simply by thinking about moving the cursor, without any motor output from the monkey.

There have also been experiments in humans utilizing modern invasive and non-invasive brain imaging technologies as interfaces. The most commonly studied potential interface for humans has been electroencephalography (EEG), mainly due to its fine temporal resolution, ease of use, portability, and cost of set-up. However practical use of EEG as a BCI requires a great deal of user training and is highly susceptible to noise. Magnetoencephalography (MEG) and even functional magnetic resonance imaging (fMRI) have both been used successfully as rudimentary BCIs, in the latter case allowing two users being scanned in real-time to play Pong against one another by altering their haemodynamic response through various biofeedback techniques.

Simple brain-computer interfaces already exist in the form of neuroprosthetics, this is an area of neuroscience concerned with neural prostheses, developing artificial devices to replace or improve the function of an impaired nervous system. The differences between BCIs and neuroprosthetics are mostly in the ways the terms are used: “neuroprosthetics” typically refers to clinical devices, whereas many BCIs are still in the experimental realm. Practical neuroprosthetics can be linked to any part of nervous systems, for example peripheral nerves, while the term “BCI” often designates a narrower class of systems which interface with the brain directly. The terms are sometimes used interchangeably and for good reason. Neuroprosthetics and BCI seek to achieve the same aims, and both use similar experimental methods and surgical techniques. However while former concerns itself with recovering lost functionality, the latter is working towards the goal of providing a universal interface that can be used by everyone.

Brain-computer interface experiments involve considerable system support. A typical setup includes four areas requiring detailed attention: preparation, presentation, detection and control. Preparation includes all those activities required to adjust the detection apparatus, both generally and for particular subjects, and to prepare the subject to participate. This may require donning special headgear, mounting electrodes (in some cases implanting electrodes surgically) or attaching sensing elements to the subject in other ways. Presentation includes all those activities involved in stimulating the subject in order to elicit the particular brain response under study. Most presentation techniques today involve the visual field in some way, partly due  to the site of relevant brain activity (visual cortex) lying closer to the scalp than several other important sites, and partly due to the availability of  inexpensive computer display devices. Detection includes all those activities and mechanisms involved in recording and analyzing electrical signals (event-related potentials or ERP’s) from the sensing elements attached to the subject quickly and reliably enough to not be a controlling factor in the experimental design. Most techniques involve the electroencephalogram (EEG) in some in some way and may include wave-form analysis which can be carried out with an inexpensive computer fitted with suitable digital signal processing (DSP) apparatus, software and display components, usually the same machine that presents the stimulus. Control includes all of those uses of detected subject responses for manipulating the environment in some desired way.

Feedback is a critical part of this activity. Slow shifts of cortical potentials occur when a subject imagines an event or imagines movements of different limbs will cause changes in oscillatory EEG activity over sensorimotor areas of the central cortex. The resulting signal is used for biofeedback to improve the training effects which will eventually  generate a control signal for communication. The training can be quite lengthy, and there is no real way to describe to the subject exactly what must be done.

At the same time the system must also be trained to react appropriately to the signals it gets from the subject. An interesting question for the development of a BCI is how to handle these two learning systems: The machine should learn to discriminate between different patterns of brain activity as accurate as possible and the user of the BCI should learn to perform different mental tasks in order to produce distinct brain signals. BCI research makes high demands on the system and software used. Parameter extraction, pattern recognition and classification as well as the generation of neurofeedback for a successful training of the user has to run in real-time.  Noise is one of the biggest problems affecting BCI’s and a great deal of  technical effort has gone into reducing its impact, however a great deal more remains to be done.

Judging from scientific papers published in technical journals and at conferences, BCI has seen increasing interest since 2000, likely due to the increased availability of inexpensive but powerful computing platforms, and the development of non-invasive sensors that can detect usable neural potentials through the skull.  While most current studies emphasize the benefits to the handicapped as the driving  force for this research, a great deal of interest has grown in the gaming and haptic feedback communities and it is likely that funds and progress can be expected from those quarters as well.

For those who would like to experiment on their own, several consumer products are available from sites like: http://www.ibva.com/. A Google search will turn up others.