4.6: Brain-Computer Interfaces (BCI)

BCI Functions

Brain-Computer Interface (BCI), also known as Brain-Machine Interface (BMI) (Lebedev & Nicolelis, 2006; Kansaku, 2011; Takano, Hata & Kansaku, 2011), Mind-Machine Interface (MMI), Synthetic-Telepathy interface (STI) (Bogue, 2010), and Neural Interface System (NIS) (Hatsopoulos & Donoghue, 2009), is a new technology that provides a promising way of communication, especially for neurologically disabled people. This interface does not rely on any physical movement. Instead, electrical signals from the cerebral cortex (Hill, Brunner & Vaughan, 2011) are recorded and translated in order to control external devices. Essentially, users are exposed to a stimulus for making a choice or instructed to actuate a certain task, so that neurophysiological signals are elicited via a signal acquisition technique. In doing so, the connection between the nervous system and a machine is established. This connection provides the opportunity for users to not rely on their muscles or peripheral nerves to operate devices.

Different Types of BCI

In order to elicit signals from brain activity, two main techniques can be mentioned, which are self-actuated BCIs and stimulus-driven BCIs. Self-actuated BCI is also known as self-paced BCI (Scherer, Chung, Lyon, Cheung & Rao, 2010).

Self-actuated BCI

For self-actuated BCI, there are no certain stimuli. In this technique, the users determine the time in which they begin and stop to operate a certain mental task (Hill et al.,2011). Mental imagery, is a phenomenon in which a subject imagines to perform a given action, could be given as an example of self-actuated BCI. Scherer et al. (2010) used mental imagery as a control signal for simple commands. The users operated a Virtual Environment by thinking of moving their left hand, right hand, foot, and tongue. For instance, in order to make a right turn, they thought of their right hand. Elicited signals from the motor and pre-motor cortical areas via EEG enabled it to control the joystick. In doing so, the users achieved to find coins dispersed in the immersive virtual environment.

The disadvantage of regulating users’ own brain activity (self-actuated BCI) is long-term training for controlling the system. What is more, Hill et al. (as cited in Hofmann, Vesin, Ebrahimi & Diserens, 2007) showed that completely locked-in patients could not benefit from mental-imagery based BCI system, since the elicited signals were not satisfactory for communication.

Treder and Blankertz (2010) developed a two-level typewriter called Hex-O-Spell layout, as an alternative to the classical matrix speller to eliminate crowding effect.

Figure 1 Hex-O-Spell layout (taken from Treder & Blankertz, 2010)

In the Hex-O-Spell layout, numbers and symbols are arranged into a radial layout. There are six circles placed to create a hexagon. It is the first level of this layout. If one of these circles is selected, 5 symbols in a circle expand into another circle. In the second level, one circle remained empty for the purpose of returning to the first level in case of making a mistake. Users achieved to copy given German words by giving their attention to target symbol located in a circle (Treder & Blankertz, 2010) (figure 1).

Stimulus-driven BCI

For stimulus-driven BCI, there must be an external sensory stimulus to record signals. Unlike self-actuated BCI, the time for performing a task is determined by experimenters. When a user focused on a prescribed stimulus, BCIs can distinguish it with larger ERPs from given response to another stimulus. P300 based BCI (P300 speller or Donchin matrix speller) is an example of stimulus-driven BCI. The visual stimulus is a matrix consisting of letters and other symbols. The user focuses on one of the flashing icons in a 6 by 6 matrix. Each column and row of the matrix are highlighted in a random order. If the row or the column contains the chosen letter, a positive deflection in ERP is drawn out after roughly 300 milliseconds (Müller-Putz, Scherer, & Pfurtscheller, 2007). In this technique, elicited control signals rely on the oddball paradigm that is a method in which more than one stimulus is presented, with one stimulus appearing less than the other stimuli.

Scherer et al. (2010) used the P300 based visual evoked potential to give commands to the humanoid robot, which has the ability to move, grasp, and release objects, to interact with the environment in AR. The user was able to see the humanoid robot’s environment through the robot’s camera. In this way, the user could select an action based on objects in the image. When the user focused on the desired object, images are flickered. After the flicker occurred on the desired objects, the system recognized the given response and considered it as the user’s choice.

Kansaku (2011) and Takano et al. (2011) developed a new BCI system by adding Augmented Reality (AR). To make a control panel, see-through Head-Mount Display (HMD) was added to the system as well. A USB camera attached to see-through HMD was used for detecting the AR marker. When an AR marker is detected by the camera, control panels that were a lamp or a TV appeared on either LCD monitor or see-through HMD. On this control panel, icons were used to control the device. Green and blue flickers instead of white and black flickers were preferred because Kansaku (2011) found that it provides a better subjective experience and better accuracy. In addition to this, Perra et al (as cited in Kansaku, 2011) confirmed that blue and green are the safest color combination for people with photosensitive epilepsy. The user focused on an icon with an action on it, such as turning on the lamp. The brain waves that are measured with the electrode cap were recorded and then the icon turned green and the desired action was performed.

Piccione et al. (2006) used P300 based-BCI by recruiting disabled and physically capable people. They were instructed to move a blue ball from the starting point to the endpoint on the screen by focusing on one of the presented visual stimuli, which were four arrows. Intensification of an arrow in each trial was 150 milliseconds. Each arrow is randomly flickered every 2.5 seconds (interstimulus interval). Users accomplished to control a two-dimensional cursor. After each trial, researchers expected to elicit P300, if the users were able to focus on the target arrow. Elicited signals were classified by using some signal processing and mathematical procedures such as amplification and a band-pass filter.

Sellers and Donchin (2006) used P300 with a four-choice paradigm and showed that ALS patients achieved to control BCI system. However, in this study, the obtained communication rates were low compared to state-of-the-art BCI (Hofmann et al.,2008). The cause of low communication rate was related to a few presented stimuli and relatively long interstimulus interval such as 2.5 seconds. In order to increase classification accuracy and communication rates, Hofmann et al. (2008) used a six-choice paradigm. Also interstimulus interval in their study was 400 ms. The reason for using a six-choice paradigm instead of a four-choice paradigm is that more stimuli decreases the probability for the target stimulus to be recognized easily. According to results of this experiment, all disabled users accomplished 100% classification accuracy as researchers had expected.