**1.3. Motor imagery**

Sensorimotor rhythms (SMRs) are synchronized brain waves over sensorimotor cortex in three different frequency bands: μ (8–12 Hz), β (18–30 Hz), and γ (30–200 Hz). EEG recording is mostly limited to μ and β bands. SMR amplitude is higher during idle stage called as event-related synchronization (ERS) and the amplitude decreases when the sensorimotor areas are active due to a certain motor task or even during motor imagery (MI). This decrease in SMR amplitude is called event-related desynchronization (ERD). The ERD signal is used for MI-related BCI. ERS immediately occurs after ERD [29]. For MI tasks, the subjects are instructed to imagine themselves performing a specific motor action without actual motor output and there exists contralateral lateralization of left-hand/right-hand/foot [30].

A novel typewriter "Hex-O-Spell" was presented in [31] using imagined right-hand and right foot movements shown in **Figure 3**. Five letters or symbols are inside six hexagons surrounding a circle having center arrow. Imagination of right-hand movement turns arrow clockwise and imagination of right foot movement stops the rotation and arrow extends to select a character if the imagination persists longer. A synchronous MI-based "Oct-O-Spell" paradigm is designed by [32] using 2-D cursor control with simultaneous MI tasks and claimed to be feasible with higher efficiency.

**2. Hybrid BCI and modes of operation**

eight trials with 10 healthy subjects.

The initial concept of hybrid BCI was used in [49] to incorporate electrocardiogram (ECG) with EEG for autonomous BCI switch ON and OFF operation to analyze whether heart bit rate can be used as an additional communication channel in BCI. P300 was combined to μ and β rhythms from sensorimotor cortex to operate a brain-controlled wheelchair [50]. In [51], hybrid P300/ SSVEP system was compared with conventional P300 and SSVEP BCI from 10 healthy subjects and observed improved performance relative to single SSVEP system and the user acceptability was higher for the hybrid which suggested the need for efficient future protocols. A continuous simultaneous hybrid BCI for two dimensional cursor control was introduced in [52] using ERD and SSVEP activity, in which vertical position of the cursor was controlled via ERD with imagined movement and the horizontal position with SSVEP from visual attention, and

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the overall result suggested that further research is needed to optimize hybrid BCI.

In [53], hybrid BCI systems were reviewed and different possibilities to combine their advantages and disadvantages were discussed. Hybrid P300/SSVEP was used by [54] for GO/STOP command in wheelchair control at simultaneous asynchronous mode and obtained improved performance in terms of detection accuracy and response time. A novel hybrid P300/SSVEP was designed by [55] integrating random flashing and periodic flickering to reduce adjacency problem and habitual repetition, and obtained an online classification accuracy of 93.85% and information transfer rate of 56.44 bit/min from 12 healthy subjects in a single trial. A new hybrid P300/SSVEP was proposed in [56] based on visual approach of shape changing instead of existing color changing and compared the performances with traditional P300, SSVEP, and normal P300/SSVEP hybrid. The new hybrid BCI was compared with normal hybrid and each traditional BCIs, and found better performance with 100% accuracy and 30 bit/min ITR for

A systematic review of hybrid BCI was done by [57] in terms of taxonomy and usability. This review discussed two modes of operation: simultaneous and sequential modes. In simultaneous mode, any two BCI systems (e.g., P300 and SSVEP) work simultaneously controlling two functions at a time and this combined system might achieve higher accuracy and ITR. As explained previously in [52], the hybrid BCI used simultaneous mode which includes ERD (imagined movement) to control the cursor in vertical position and SSVEP to control the cursor in horizontal position. In sequential mode, output of one BCI system is used as the input for another to control various functions of the second BCI system or as a switch in asynchro-

Among all other EEG signals, SSVEP possess a better suitability to combine with P300 [58] for

• SSVEP and P300 both are elicited by visual stimuli, so subjects only need visual attention.

• Both are noninvasive so reduction in experimental setup time, complexity, effort, and cost.

nous mode [57]. These two modes are depicted in **Figure 4a** and **b**.

constructing efficient hybrid BCI due to the following reasons [55]:

• No mental count is required for SSVEP thus reducing the mind workload.

**Figure 3.** Two states of "Hex-O-Spell" paradigm selecting a character using MI [31].

MI detection is challenging due to low signal-to-noise ratio, but development of advance signal processing enables MI-based BCI to implement various tasks [33]. MI-based BCI was used first time by [34] for stroke rehabilitation in a tetraplegic patient using imagination of foot movement where the patient was able to grasp cylinder with the paralyzed hand.

MI-based BCI is a system that is subject specific and requires data recording and a system training for each new user. Subject-independent MI was developed by training the data acquired from several subjects [35] and a conscious target strengthens ERD in β frequency band [36]. ERD amplitude was higher due to body ownership illusion like moving rubber hand than other visual targets [37].

MI activity acts as a neurofeedback and a feasible part of stroke rehabilitation but may increase moderate fatigue due to external factors like long hours of training session [38]. Neural plasticity can be achieved through neurofeedback [38, 39]. MI-based BCI uses a neurofeedback strategy in poststroke rehabilitation using functional electrical stimulation (FES), robot, and orthosis [40]. Majority of stroke patients can use EEG-based MI [41, 42] for limb rehabilitation [43] and was extended to imagination of tongue movement [44]. MI can be used for a reliable and high performance BCI for both healthy subjects and ALS patients where the user requires less trainings [45]. MI-based BCI can be used for stroke rehabilitation to perform various functions such as controlling computer cursor, processing word, accessing Internet, and controlling environment and entertainment [33]. Without any muscular activities, MI tasks were employed in an experiment to drive a car in 3-D virtual environment [46] and to play video game on virtual ground [47].

There are other methods apart from EEG to measure brain activities such as magnetoencephalography (MEG), electro-corticography (ECoG), functional magnetic resonance imaging (fMRI), and functional near-infrared imaging (fNIR). However, due to noninvasive method, easy experimental setup, low cost, and high efficiency, EEG is most widely used. Although P300, SSVEP, and ERD/ERS are most widely used EEG signals, there are also other brain signals such as slow cortical potentials (SCP) and electrooculogram (EOG) in BCI [29]. Each of these brain signals do not work same for all users. So, a novel approach has been used to combine two or more conventional BCIs to form a hybrid BCI to enhance the overall performance [48].
