4.9: Limitations and Interpretations

Regardless of whether self-actuated BCI or stimulus-driven BCI is used as a BCI strategy, researchers are tackling the limitation of BCI systems stemming from weak Human-Computer Interaction design or noisy signals due to the fact that the skull blocks signals generated in a certain area of the brain (Hill et al.,2011).

An example of a weak HCI could be the significant difference between the performance of healthy users and the performance of locked-in state patients (Piccione et. al, 2006: Sellers & Donchin, 2006). In the classical matrix speller, obtaining a high performance relies on precisely fixed eye on the target stimulus. In other words, the classical matrix speller requires overt attention (Treder & Blankertz, 2010). Nonetheless, it is very difficult for locked-in patients to fix their gaze exactly on the target stimulus in densely arranged symbols due to the crowding effect and low-spatial resolution (Hill et al.,2011). Unlike overt attention, covert attention does not rely on eye movement. It is the act to distribute the attention in visual periphery without shifting the gaze to target stimuli (Treder & Blankertz, 2010). On the other hand, since the number of densely packed cone receptors reduce beyond fovea and macula, identifying the target stimulus in visual periphery is more difficult. To find out whether or not eye movement affects the accuracy in ERP-based BCI, Treder, and Blankertz (2010) developed a stimulus in which letters are located on a radial layout that is called Hex-O-Spell self-actuated spelling system. Based on this study, Hex-O-Spell gave better results compared to the matrix speller in terms of accuracy. As researchers had expected, accuracy was better as well for over attention condition rather than covert attention condition.

An example of noisy signals is that EMG signals are affected by unexpected muscle tension. Besides, eye blinking and muscle tension have an impact upon EEGs. In order to deal with noise in EEG arising from muscle activities and eye movements, Hofmann et al., (2008) applied a statistical procedure called windsorizing. They extracted the first and the last 10 percentile of the data.

To date, the developed BCI systems are not advanced enough for people with disabilities who have trouble with everyday activities. This is because the communication rates are quite low (5-25 bits per second). In order to turn on a lamp or type a symbol in the BCI system, for example, it takes approximately 15 seconds (Takano et al., 2011). It is not time efficient and an able-bodied person would have turned on the lamp by the time the BCI system has turned it on. However, recent studies show that communication rates and accuracy promisingly increased with better research designs. In addition to BCI systems, AR systems and combination of both systems may enable us to control electronic devices in the real world environment (Navarro, 2004). Another study showed that Humanoid Robots can successfully complete particular everyday tasks such as pouring milk over a bowl by using hierarchical EEG-based BCI. Their design allows users to complete both relatively complex and basic tasks by using brain signals (Bryan et al., 2011). ALS patients or person suffering from voluntary muscle control problems may transport himself/herself with an EEG-based BCI adapted wheelchair (Iturrate et al., 2009). Researchers are also examining the combination of VR, AR, and BCI systems to improve rehabilitation and therapy techniques. For example, Mirror Box Therapy (MTB) displays illusory movements which correspond to patient’s brain activity to move healthy or injured limbs (Regenbrecht et al., 2014).

With the improvement of the Human-Computer Interaction design in BCI, signal acquisition techniques and BCI strategies, new BCI systems will facilitate both able-bodied and disabled people’s everyday activities. Perhaps, the integration of neurologically disabled people and amputees into society will be possible with proliferated intelligent environments especially in hospitals, nursing homes, and rehabilitation centers. It will help them operate devices without using muscles or experience cultural and natural activities in an intelligent environment. For instance, an ALS patient could experience mountain climbing thanks to the virtual environment. Likewise, an amputee could totally control his artificial limb via an invasive BCI system in the future.