Relaciones espaciales

The idea of an EEG-based communication system was first introduced in the 1970s by Vi- dal [Vidal, 1973, 1977]. But the first BCI prototypes became feasible only recently with the discovery of the mechanisms and spatial location of many brain wave phenomena and their relationship with specific aspects of brain function. Furthermore, the development of powerful computers made possible the creation of systems able to perform the online analysis of mul- tichannel EEG. Up to date the main applications of EEG-based BCIs are the operation of a virtual keyboard for letter selection on a computer screen [Birbaumer et al., 1999, Obermaier et al., 2003, Wolpaw et al., 2003, Mill´an et al., 2004b], the basic control of a hand prosthe- sis [Pfurtscheller et al., 2000] and the control of a wheelchair in an indoor-like environment [Mill´an et al., 2004a, Philips et al., 2007]. As already mentioned in the Introduction of this the- sis, a BCI monitors the user’s brain activity and translates their intentions into actions without using activity of any muscle or peripheral nerve. The central tenet of a BCI is the capability to distinguish different patterns of brain activity, each being associated to a particular intention or mental task. Such a kind of BCI is a natural way to augment human capabilities by pro- viding a new interaction link with the outside world and is particularly relevant as an aid for paralyzed humans, although it also opens up new possibilities in natural and direct interaction for able-bodied people. Figure 1.1 shows the general architecture of a non-invasive EEG-based BCI. Brain electrical activity is recorded with a portable device. These raw signals are first processed and transformed in order to extract some relevant features that are then passed on to some mathematical models (e.g., statistical classifiers or neural networks). This model com- putes, after some training process, the appropriate mental commands to control the device. Finally, visual feedback, and maybe other kinds such as tactile stimulation, informs the subject about the performance of the brain-actuated device so that they can learn appropriate mental control strategies and make rapid changes to achieve the task.

Present-day non-invasive BCIs can be classified into 4 groups according to the electrophys- iological signals they use. The first group, using visual evoked potentials (VEP), are said to be dependent BCIs because they depend on peripheral nerves or on muscular control. The other three groups, using slow cortical potentials (SCP), P300 evoked potentials, and mu and beta rhythms, are said to be independent BCIs because they do not depend on peripheral nerves or muscles, but this assumption is sometimes controversial. Invasive BCIs using electrodes di- rectly implanted in the motor cortex of monkeys and humans have provided stable neuronal recordings and encouraging results [Chapin et al., 1999, Carmena et al., 2003, Musallam et al., 2004, Kennedy and Bakay, 1998, Kennedy et al., 2000, Hochberg et al., 2006], but non-invasive techniques are necessary for humans. For reviews on BCIs see [Mill´an, 2002, Nicolelis, 2001, Wickelgren, 2003, Wolpaw et al., 2002].

2.5.1

Visual evoked potentials

The most widely used evoked potentials in BCIs are visual evoked potentials that are EEG waveforms generated in response to visual stimuli that can be used to determine the direction of eye gaze. The BCI developed by Vidal in the 1970s [Vidal, 1973, 1977] using visual evoked potentials satisfy the current definition of a dependent BCI. This system basically use visual evoked potentials recorded from the scalp over visual cortex to recognize the direction of the visual fixation point and so, for instance, can be used to determine the direction in which the subject wishes to move a cursor. When the repetition rate of the visual stimulus is faster than 6 Hz, the new stimulus is presented before the last response of the visual system vanishes, a periodic response called steady state VEP (SSVEP) is generated. SSVEP are usually used in the short-term identification of evoked responses because of its high signal-to-noise ratio (SNR) [Sutter, 1992, Middendorf et al., 2000, Gao et al., 2003].

2.5.2

P300 evoked potentials

P300 is a positive deflection in the EEG occurring about 300 ms after an infrequent or partic- ulary significant stimulus interspersed with frequent stimuli. This stimulus can be of various nature: visual, auditory or somatosensory. Donchin and his colleagues have used this P300 potential to design a BCI [Farwell and Donchin, 1998, Donchin et al., 2000]. P300-based BCIs have the advantage that they require no prior training as P300 is a typical response to a desired choice. Nevertheless, the amplitude of P300 diminishes over time so that the performances of the BCI could decrease. Another drawback of P300-base BCIs and any kind of BCI based on evoked potentials is the dependency on external stimulations. The variety of tasks is limited and the subject only sustains the stimuli instead of being in total control on the interaction.

2.5.3

Slow cortical potentials

Slow cortical potentials (SCP) are slow voltage changes lasting from 500 ms to a few seconds. Negative SCP are typically associated with movements and other functions involving cortical activation whereas positive SCP are usually associated with reduced cortical activation. Differ- ent studies have shown that people can learn to control SCP and so control movements of an object on a computer screen [Birbaumer et al., 1999, Elbert et al., 1980]. This demonstration is the basis for a BCI referred to as a “Thought Translation Device” (TTD) [Birbaumer et al., 1999].

2.5.4

Mu and beta rhythms

In awake people, primary sensory or motor cortical areas often display 8-12 Hz EEG activ- ity called mu rhythms. These mu rhythms usually appear in conjunction with 18-26 Hz beta rhythms. These rhythms could be good signal for EEG-based communication. Movements or preparation of movements are associated with a decrease in mu and beta rhythms. This decrease has been named event-related de-synchronization (ERD) [Pfurtscheller and da Silva, 1999]. The opposite increase in rhythms (named event-related synchronization, ERS) occurs after movements and during relaxation. The most important property of ERD and ERS is that

they occur also with motor imagery and so they could be used in the framework of an indepen- dent BCI. Several groups have developed mu/beta rhythms-based BCIs [Wolpaw et al., 2003, Pfurtscheller and Neuper, 2001, Kostov and Polak, 2000, Penny et al., 2000].