Intro

The brain is the center of the nervous system. In vertebrate is the most complex organ of its body. In a typical human the cerebral cortex is estimated to contain 15–33 billion neurons, each connected by synapses to several thousand other neurons. These neurons communicate with one another by means of long protoplasmic fibers called axons, which carry trains of signal pulses called action potentials to distant parts of the brain or body targeting specific recipient cells. In a philosophical reductionist approach, the brain could be defined as the source of all the environment perceptions, the intelligence and the mind. When large numbers of neurons show synchronised activity, the electric fields that they generate can be large enough to detect outside the skull using Electroencephalography (EEG).

EEG is the recording of electrical activity along the scalp allowing measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. The use of EEG signals as a vector of communication between animals and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, more known as Brain Computer Interface (BCI), is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Some research groups have used terms such as “passive BCI”, “affective BCI”, “emotive BCI”, or “mental state monitor” to describe devices and methods that directly measure brain activity, and often provide real-time feedback in a implicit or explicit way. The conventional and new definitions generally differ on whether passive monitoring tools are BCIs. The above definitions also generally conflict with each other on issues such as whether real-time interaction or enhancing human-computer interaction is required.

Any BCI has four components: signal acquisition (getting information from the brain); signal processing (translating information to messages or commands); devices and applications (such as a speller or robotic device); and an application interface (or operating environment) that determines how these components interact with each other and the user. Rapid progress is being made in all four components. New sensors are being developed that do not require electrode gel, which reduces preparation time and makes BCIs more accessible to new users. Dry sensors over the forehead can acquire not only brain signals, but also other relevant signals such as Electrooculography (EOG) and Electromyography (EMG). Companies like Quasar, Emotiv and NeuroSky have dry electrode and low-cost systems for gaming and other goals. The architecture of meme framework was designed to support the implementation of some of these BCIs devices.

The analysis of continuous EEG signals or brain waves is complex, due to the large amount of information received from every electrode. Different waves, like so many radio stations, are categorised by the frequency of their emanations and, in some cases, by the shape of their waveforms. Although none of these waves is ever emitted alone, the state of consciousness of the individuals may make one frequency range more pronounced than others. In EEG, brain-related electrical potentials are recorded from the scalp. Pairs of conductive electrodes are used to read this electricity. The differences in voltage between the electrodes are measured, and since the signal is weak it has to be amplified. Current, measured in microvolts (μV), occurs when neurons communicate; the simplest event is called action potential, the discharge caused by fast opening and closing of Na+ and K+ ion channels in the neuron membrane. If the membrane depolarize to some threshold, the neuron will ”fire”. Tracking these discharges over time reveals the brain activity.