BRAIN-COMPUTER INTERFACE

Brain-computer interface (BCI) is an advanced method that connects our natural brain with and an external device, providing a new communication channel for brain signals to control external devices without using the natural neuromuscular routes. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions (He, Baxter, Edelman, Cline, & Ye, 2015; Krucoff, Rahimpour, Slutzky, Edgerton, & Turner, 2016). By detecting the brain's signals and interpreting them, BCI allows generating control commands for external devices. The detection of the brain signals is performed either by the recording of its electrical activity or the related magnetic activity (Lange, Low, Johar, Hanapiah, & Kamaruzaman, 2016). A BCI-based system contains at least four main steps: (1) extracting signals from the nervous system, (2) decoding the signals to predict user intent, (3) generating an output to affect the subject’s environment, and (4) providing a feedback system to help the user refine the output (Krucoff et al., 2016). Figure 1 shows how BCI operates in a system and its input and output signals.

The benefit of BCI is the independence from any remaining muscular functions, which means that muscle fatigue is irrelevant. So, it can be used to restore lost or impaired functions of patients severely disabled by various devastating neuromuscular disorders or patients with damaged nervous systems (Lange et al., 2016; Lazarou, Nikolopoulos, Petrantonakis, Kompatsiaris, & Tsolaki, 2018). BCI can promote long-lasting recovery in the motor function of chronic stroke patients with severe myasthenia and represents a promising strategy in severe stroke neuro-rehabilitation (Ramos-Murguialday et al., 2019). Combined with BCI, the signal of the brain evoked by the individuals’ spontaneous motor imaginary can be extracted and classified to directly control the robot. With this collaboration, robotic therapy can discover an artificial pathway to replace the normal motor pathway of humans (Teo & Chew, 2014).

BCIs have the potential to provide two key benefits to disabled users: an alternate means of communication, and the ability to independently move around in and interact with their environment (He et al., 2015). The use of BCI in stroke rehabilitation can be divided into two main approaches: a monitoring mechanism, with recorded brain signals serving as feedback data for concentration levels of the therapeutic practice; and a control framework in which an artificial actuator is driven at demand (Maier, Ballester, & Verschure, 2019).

ELECTROENCEPHALOGRAPHY

Non-invasive BCIs relate to methods that extract brain signals without surgical procedures. The experimental setup ranges from large and relatively expensive options, like magneto-encephalography (MEG), to lightweight and inexpensive methods, like electroencephalography (EEG). Due to its availability and ease of use, EEG is widely used in clinical stroke rehabilitation (He et al., 2015; Yuan & He, 2014). This method is performed by measuring the electric field on the patient's scalp through electrodes. The signal of the patient is not enough to actuate the motion of the paretic limb, but it is strong enough for the data acquisition instruments to collect (Lange et al., 2016). EEG-based BCI operates by establishing a closed-loop neural interface. In other words, a BCI system uses raw functional cortical activity generated by EEG and translates it into a classified device command (Kreilinger et al., 2012; Soekadar, Birbaumer, Slutzky, & Cohen, 2015). Also, signal input is amplified and processed by a regression model that extracts particular amplitude changes or features and accounts for signal noise (Kasuga et al., 2015; Young et al., 2014). With real-time processing, the reduced representation of brain activity is effectively translated into an output or feedback modality, often one that allows the desired task to be performed more easily (Remsik et al., 2016). Some features of EEG-based BCI systems are:

• To monitor and control brain activity of patients to improve their neural representation

• To facilitate arm mobilization with higher motor gain for paralyzed arms and hands

• Movement intention decoding performance and motor recovery

• To engage the patients with tasks and acquire cortical signal of their imagination of a movement

By using EEG-based BCI, voluntary movement or motion attempt of the limb activates the primary sensorimotor section in the brain, which is characterized by specific brain rhythms over the hemisphere contralateral to the limb in use (Ang et al., 2015). EEG signals can be controlled by BCI to aid the patient, either by bypassing a physical impairment in the ability to control the limb or by highlighting engagement with a movement (Ron-Angevin et al., 2017). In (Barsotti et al., 2015; Frisoli et al., 2012) , EEG was used as a data acquisition system to record activity during BCI-training to support tasks like preparation of movement, reaching, grasping, and releasing. This method was also used to study the effect of BCI control of passive motion for stroke patients while they were imagining a reach and grasp movement using the affected limb (Lu et al., 2020; Pereira, Sburlea, & Müller-Putz, 2018). EEG-based BCI was used in (Chowdhury, Raza, Meena, Dutta, & Prasad, 2019) to estimate the quality of engagement of a patient with tasks by measuring the relationship between the brain and the muscle signals. In (Spüler, López-Larraz, & Ramos-Murguialday, 2018), the effects of decoding performance on movement intention in a BCI system was studied by varying the cortical source of activity and the EEG frequency band in stroke patients.

FUNCTIONAL ELECTRICAL STIMULATION

Functional electrical stimulation (FES) is a subtype of neuromuscular electrical stimulation, which uses low-energy electrical pulses to artificially generate body movements. It can be used to generate muscle contraction in paralyzed limbs to produce functions such as grasping, walking, and standing (Moineau, Marquez-Chin, Alizadeh-Meghrazi, & Popovic, 2019). In FES, the electrode mainly operates as a conductor, sending electrical charge from a power supply over the tissue. When this applied voltage between the active electrode and a second electrode generates an electric field, charge transfer occurs, which as a result, forces electrical charge to flow ( (Ho et al., 2014) Ho et al., 2014). Then, this electrical current elicits a response in excitable cells including neurons. Cochlear implants to restore hearing, phrenic pacemakers that aid respiration, cardiac pacemakers to ensure cardiac function, and deep brain stimulation to control tremors due to Parkinson’s disease are examples of applications of electrical stimulation systems ( (Marquez-Chin & Popovic, 2020)).

Nowadays, FES-BCI systems are increasingly being explored as potential rehabilitation tools for improving the function of partially impaired limbs ( (Do, Wang, King, Abiri, & Nenadic, 2011) ). FES can use real-time feedback of BCI signal input to optionally administer feedback responses of treatment only when the correct brain signals are discovered. Using FES in robotic therapy can lead to a better realization of the recovery of limb function ( (Rong, Tong, Hu, & Ho, 2012) ). Some features of FES-BCI systems are:

• To generate muscle contractions and subsequent limb movement

• To provide functional recovery and elicit motor recovery

• To adjust the intensity of the electrical stimuli to achieve more precise movements

• To improve functional mobility and range of motion of flexion and extension muscles exercises

Neuro-rehabilitation methods employing FES to maneuver the paralyzed muscles can postpone or prevent many secondary medical complications and modify functional independence by providing a means to exercise and practice physical obstacles (Ho et al., 2014). These methods can be used to improve functional mobility and range of movement of paretic stroke patients (Kim, Kim, & Lee, 2016). FES-BCI training can be beneficial on shoulder reduction and active movements by facilitating motor recovery (Jang, Kim, & Lee, 2016). In (Grimm et al., 2016), the effects of this method were examined on the range of motion and cortical modulation in stroke patients who performed wrist flexion and extension exercises. BCI–FES can provide high functional recovery in patients with a high degree of residual mobility in arm and hand function and also elicit motor recovery in stroke patients (Biasiucci et al., 2018). To perform daily living activities, BCI with a neural decoder can be used to produce coordinated reaching andgrasping movements and FES to generate muscle contractions and subsequent limb movement (Ajiboye et al., 2017). In (Bockbrader et al., 2019), the ability of FES-BCI was examined in evoking greater wrist extension strength and controlling translation, orientation, and hand shape of robotic limbs.