2.1. BCI Types
A BCI provides an interconnection platform that supports the full duplex communication between the brain and an external device. According to the way that BCIs use to set up the brain–device interconnection, they are classified as non-invasive or invasive. Non-invasive BCIs use electrodes placed on the scalp. They are easy and safe to use, low-cost, portable, and offer a relatively high temporal resolution. Invasive BCIs use electrodes implanted in the interior of the scalp. Comparatively to non-invasive BCIs offer higher values of amplitude, spatial resolution, and resistance to noise. However, they require neurosurgery operations and they are both unsafe and expensive. Furthermore, scar tissues decrease the quality of signals received. Practically, non-invasive BCIs are used more often.
There are various non-invasive methodologies used in BCI technology, such as Positron Emission Tomography
(PET), functional Magnetic Resonance Imaging
(fMRI), and Near-Infrared Spectroscopy
(NIRS), which study changes made in the blood flow, magnetoencephalography
(MEG), which monitors the magnetic action of the brain, and EEG, which records the electric activity of the brain. Both NIRS and fMRI BCIs offer high spatial resolution, but poor temporal resolution. Moreover, MEG and PET BCIs offer high spatial and temporal resolution. However, PET BCIs require the inoculation of a radioactive constituent into the bloodstream. Furthermore, both fMRI and MEG methods rely on the use of equipment which is not only costly, but also huge. EEG BCIs are by far the most popular type, because, despite their relatively poor spatial resolution, they have high temporal resolution, low-cost, and easy installation. [6
Moreover, BCIs are classified as either exogenous or endogenous, according to the nature of the input signals. Exogenous
BCIs analyze the brain activity created due to external stimuli. They are easy to set up and offer high bit rates, but they need the continuous response of the user to outward incitements which may be either tiring, or even unfeasible. Endogenous
BCIs use self-regulation of brainwaves without external stimuli. They provide lower data transfer rates but they can be operated via free self-control even by users with sensory organs affected or suffering from motor neuron diseases [10
Similarly, BCI systems are classified, according to the method used for input data processing, as synchronous or asynchronous. Synchronous
BCIs analyze the brain signals only after a specific prompt and during predefined time intervals. Thus, the overall process is better organized and the user is free to make any kind of movements, which would produce artifacts, when brain signals are not observed. They also require minimal training and have stable performance and high accuracy. Asynchronous
BCIs inspect brain signals successively, thus letting the user act at free will. Therefore, they offer more natural human–machine interaction. However, they are more complex in design and evaluation and require extensive training. Moreover, their performance may vary between users, and their accuracy is not very high [10
2.2. Brainwaves for EEG-BCIs
The most commonly used types of brain waveforms to develop EEG-based BCIs are P300, SSVEP, ErrP, ERD/ERS, and alpha brainwaves [11
P300 is an event-related positive potential deflection which is caused by the reaction to a desired external stimulus of visual, auditory, or tactile modality. P300 waveforms are typically measured, with a latency of roughly 250 to 500 ms between stimulus and response, by using electrodes located over the parietal lobe of the scalp.
Steady state visually evoked potentials (SSVEP) are brain waveforms of exogenous type that are generated as responses to visual stimulation at specific frequencies ranging from 3.5 Hz to 75 Hz. Considering that SSVEP signals often have their highest values at medial occipital electrode sites, they are supposed to originate mostly from the primary visual cortex.
Event-related desynchronization and event-related synchronization (ERD/ERS) waves are endogenous brain signals, which are generated when performing mental tasks, such as motor imagery or mental arithmetic. They can be measured at different cortical locations.
Error-related potential (ErrP) waveforms are brain signals which are activated every time that a subject identifies the commitment of an error which has been made either by himself/herself or by another individual during various choice tasks. Waves of this kind can be captured by applying electrodes on various brain regions including the anterior cingulate cortex, anterior insula, inferior parietal lobe, and intraparietal sulcus, as well as other regions of the cortex, subcortex, and cerebellum.
Alpha brainwaves are brain signals which have their amplitude increased whenever the eyes of an individual are closed during wakeful relaxation. In contrast, the amplitude of alpha waveforms is diminished for the duration of sleepiness and sleep and also when having eyes opened while mental effort is performed. This phenomenon is usually referred to as alpha rhythm blocking. Alpha brain waveforms can be monitored by applying a number of electrodes on both sides of the posterior segments of the scalp where the occipital lobe, which is the center of visual processing activities in the brain, is positioned.
2.3. BCI Operation
The operation of a typical BCI system is based on the sequential execution of a number of procedures, which namely are signal acquisition, preprocessing, feature extraction, classification, translation, and feedback to operator [10
], as shown in Figure 1
In EEG-BCIs, signal acquisition
is performed by using electrodes which are positioned along the scalp of the user. Normally, the settlement of electrodes on the scalp is performed in compliance to the International 10–20 system. According to this system, electrodes are located on the scalp at 10% and 20% of a measured distance from reference spots including nasion, inion, left, and right preauricular [10
The pattern of this system is depicted in Figure 2
, where odd numbers refer to the left side of the head, even numbers refer to the right side, A1 and A2 refer to the earlobes and ‘Fp’, ‘F’, ‘T’, ‘C’, ‘P’, and ‘O’ stand for the prefrontal, frontal, temporal, central, parietal, and occipital areas of the brain, correspondingly.
is the procedure which is carried out in order to reduce the noise from the signal and apply some filtering and other methods in order to remove artifacts which are caused by endogenous sources, such as motions of eyes, muscles, and heart, and exogenous sources, such as power-line coupling and impedance mismatch [12
]. Preprocessing is usually performed by using low-pass, high-pass, band-pass, or notch filtering. However, the use of such filters may eliminate useful elements of EEG signals having the same frequency band as artifacts [13
In feature extraction
, specific features of the signals in time domain or/and frequency domain that can expressively differentiate specific classes are extracted and positioned into a feature vector in order to enable the classification phase which follows. Autoregressive (AR), Hjorth, and EEG signal power are commonly used feature extraction techniques [14
During the classification
phase, a properly built algorithm is used. This algorithm distinguishes between classes which correspond to various brain activity patterns by deciding to which of these classes every feature vector suits best. Neural networks (NNs) are widely used as classifiers in BCIs because they provide the ability to approximate nonlinear decision boundaries [15
]. Alternatively, linear discriminant analysis (LDA), support vector machines (SVM), and statistical classifiers may be used [17
]. The advantage of LDA is that it is a simple-to-use probabilistic approach based on Bayes’ Rule. On the other hand, NNs have the advantage of being able to approximate nonlinear decision boundaries. In cases where a small amount of training data is available, the use of SVM is a very good choice. Finally, statistical classifiers have the ability to represent the uncertainty that is inherent in brain signals.
During the translation phase the extracted signal features are converted into particular commands to the device(s) under control, through the use of dedicated translation algorithms. Specifically, these algorithms have the ability not only to adapt to the continuing variations of the signal features, but also to ensure that the complete device control range is covered by the specific signal features from the user.
Finally, in the feedback to operator phase, the final outcome of the overall operation of the BCI system is transferred back to the system operator, so that the performance of the system can be evaluated.
2.4. BCI-Based Robot Control
An EEG-based brain-controlled robot is a robot that uses an EEG-based BCI to receive control commands from its human operator. EEG-based brain-controlled mobile robots can support the movement of both elderly people and people who are severely disabled with destructive neuromuscular disorders, such as amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), or strokes.
There are two main classes of EEG-based brain-controlled assistive robots which namely are brain-controlled manipulators
and brain-controlled mobile robots
. Similarly, assistive mobile robots are classified in two categories according to their mode of operation [11
The first category consists of assistive mobile robots which operate under direct BCI control. Robots of this kind are controlled exclusively via the commands that their users send to the robots controlled via BCI modules, without any additional assistance by robot intelligence elements. For this reason, they are less expensive and complex to develop and their users keep the absolute motion control.
On the other hand, the overall performance of these brain-controlled mobile robots mainly depends on the performance of the BCIs, which in many cases may have inadequate speed of response and accuracy. Furthermore, the demand for continuous production of motor control commands by the users may be extremely tiring for them.
The initial example of a robot of this kind was presented in [18
] where the left and right turning movements of a robotic wheelchair were directly controlled by corresponding motion commands translated from user brain signals.
Similarly, in [19
] a brain-controlled mobile robot was able to perform forward, left, and right motions by using a BCI based on motor imagery.
Moreover, in [20
] the motion control of a wheelchair is performed via a BCI, which captures alpha brainwaves. Specifically, a set of icons corresponding to predefined commands are sequentially displayed on a screen and the user is able to select the desired command by closing his/her eyes as soon as its corresponding icon appears on the display unit.
The second category consists of assistive mobile robots which operate under shared control. In the robots of this category the control is performed by combining a BCI system along with an intelligent controller, such as an autonomous navigation system. Due to their enhanced intelligence, robots of this type are safer and less tiring for their users and more accurate in interpreting and executing their commands. On the other hand, their development is of higher cost and computational complexity.
A typical example of shared control in assistive mobile robots is proposed in [21
]. In this system the operator, by using a SSVEP BCI system, has the ability to send commands in order to move a robotic wheelchair in four directions (forwards, backwards, left, and right), while an autonomous navigation system executes the delivered commands.
Similarly, in [22
], by using a P300 BCI, the operator uses a list of predefined locations in order to select the desired location and then sends this selection to an autonomous navigation system, which guides a robotic wheelchair to the selected location. The limitation of the specific system is that it is able to be operated only in a known environment.
Likewise, in [23
] shared control is used. Specifically, the combined use of a P300 BCI along with an autonomous navigation system is proposed in order to perform the motion control of a robotic wheelchair in an environment which is unknown. Moreover, the user has the ability to make the wheelchair turn either left or right by focusing correspondingly on one of two relative icons at a predefined visual display.
] three mental tasks, which namely are the imagination of right or left hand movements and the generation of words beginning with the same random letter, were used in a BCI system applied to a robotic wheelchair. The system developed, which interacts with the user by using a PDA screen and speakers, is able to guide the robotic wheelchair both in known and unknown environments.