**3.1. SSVEP-MI hybrid**

• Both are measured in different domains (time domain for P300 and frequency domain for

• Both are detected from almost different cranial regions with independency enabling higher

The research on hybrid BCI is growing and **Figures 5** and **6** illustrate number of publications in this field. The two figures are based on searches at IEEE Xplore [59] and PubMed [60] with keywords: "BCI," "Hybrid BCI," "SSVEP and MI," "P300 and SSVEP," and "P300 and MI "published in Conference, Journals, Magazines, Books, and e-books in the fields of

**Figures 5** and **6** illustrate the number of published articles in IEEE Xplore and PubMed which are added together. They depict the growing numbers of research in hybrid BCI and among hybrid BCIs, number of P300 and SSVEP hybrid is comparatively higher as illustrated in **Figure 6**.

"Engineering," "Psychology," "Neuroscience," "Medicine," and "Computer Science."

SSVEP).

**Figure 4.** Hybrid BCI modes of operation [57].

120 Evolving BCI Therapy - Engaging Brain State Dynamics

**Figure 5.** BCI and hybrid BCI papers in IEEE and PubMed.

accuracy.

SSVEP and ERD signals are used to form a BCI hybrid that combines visual attention and motor imagination. In [61], 14 healthy subjects (six women and eight men of ages 17–31 years) were chosen to perform three different tasks: MI only (ERD signals generated from left-hand or right-hand movement), SSVEP only (visual signals generated from two flickering LEDs at 8 Hz and 13 Hz), and simultaneous hybrid SSVEP-MI. Linear discriminant classifier was used and the classification accuracy was higher for SSVEP than MI and was highest for the hybrid.

An artificial upper limb was controlled by [62] combining SSVEP and MI in two degrees of freedom (DoF) in which MI controlled grasp function and SSVEP controlled elbow function (flexion and extension) of the limb. The experiment was conducted with 12 healthy subjects (7 male and 5 female) in offline and 7 healthy subjects (4 male and 3 female) in online. In offline experiment, 4 runs each with 40 trials were taken and the subjects were instructed to imagine feet dorsiflexion from two to four runs focusing the two flickering lights 7 and 13 Hz. The online experiment consisted a 2 DoF artificial upper limb and subjects controlled grasp and elbow functions as per instructions.

In [63], SSVEP-MI hybrid was proposed to control the speed (accelerate or deaccelerate the wheelchair based on flashing stimuli of 7, 8, 9, and 11 Hz) and direction (left- and righthand imageries to control the direction) of a wheelchair in real time. Both virtual and realtime tests were conducted to observe the performance. Three options: straight driving, left and right turns were provided for direction, and accelerate, deaccelerate, or drive options for speed control using eight separate commands: turn left, turn right, drive forward, accelerate,


deaccelerate, drive at uniform speed, and turn on or off the switch. Three healthy male subjects (21–30 years old) were participated in the experiment and the classification accuracy was more than 90% using Support Vector Machine (SVM) for MI and canonical correlation

**Table 1.** Different hybrid BCI systems' descriptions based on a number of subjects, modes of operation, classifiers used,

**Classifiers Acc.** 

P300 + MI 11 Sequential SVM 93 — Enhanced accuracy

CSP

CSP

**(%)**

**ITR (bits/ min)**

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

92 41 Control

92 — Enhanced

**Improvements Reference**

[72]

123

[73]

[74]

and lowers trial duration

http://dx.doi.org/10.5772/intechopen.75132

applications

discriminating performances

The sequential operation of SSVEP and ERD signals was used in [64] for advanced functional electrical therapy (FET) in which six right-handed healthy subjects (5 males and 1 female, mean age around 25 years) were selected. SSVEP signals from flickering LEDs of frequencies 15, 17, 19, and 21 Hz were used to select the types of grasp: palmar, lateral, and pinch followed

In [65], hybrid BCI paradigm was proposed to enhance MI tasks using SSVEP. Twenty-four right-handed healthy subjects aged 23–30 years (19 males and 5 females) were used for the experiment to perform MI focusing on flickering SSVEP, where SSVEP was used initially to train the subjects for MI tasks providing accurate feedback, and afterward, the weight was shifted to MI gradually keeping SSVEP at less weights. Common spatial pattern (CSP) was used for MI and CCA for SSVEP classifications. This paradigm hypothesized that accurate

The multiple channels hybrid was replaced in [66] combining SSVEP and MI in a single channel C3 or C4 improving performance. Seventeen healthy subjects (12 male and 5 female subjects with an average age around 23 years) were preinformed about the experiment to focus simultaneously on flickering visual stimuli of frequencies 15 and 20 Hz, and perform rightand left-hand MI, respectively. The average classification accuracy was around 85% for both

An asynchronous control of wheelchair was proposed by [54] combining SSVEP and P300 in which four groups of buttons were displayed, each group having one large central button surrounding eight small buttons with 45° spacing in a circumference of 60 mm radius. The four

by MI which was used as a brain switch that activated the intention of grasp.

analysis (CCA) for SSVEP.

**Hybrid type Subj. Modes of** 

**operation**

P300 + MI 11 Sequential SWLDA and

P300 + MI 12 Simultaneous LDA and

accuracy, ITR, and improvements in the model.

feedback enhances MI.

**3.2. P300-SSVEP hybrid**

channels.


**Table 1.** Different hybrid BCI systems' descriptions based on a number of subjects, modes of operation, classifiers used, accuracy, ITR, and improvements in the model.

deaccelerate, drive at uniform speed, and turn on or off the switch. Three healthy male subjects (21–30 years old) were participated in the experiment and the classification accuracy was more than 90% using Support Vector Machine (SVM) for MI and canonical correlation analysis (CCA) for SSVEP.

The sequential operation of SSVEP and ERD signals was used in [64] for advanced functional electrical therapy (FET) in which six right-handed healthy subjects (5 males and 1 female, mean age around 25 years) were selected. SSVEP signals from flickering LEDs of frequencies 15, 17, 19, and 21 Hz were used to select the types of grasp: palmar, lateral, and pinch followed by MI which was used as a brain switch that activated the intention of grasp.

In [65], hybrid BCI paradigm was proposed to enhance MI tasks using SSVEP. Twenty-four right-handed healthy subjects aged 23–30 years (19 males and 5 females) were used for the experiment to perform MI focusing on flickering SSVEP, where SSVEP was used initially to train the subjects for MI tasks providing accurate feedback, and afterward, the weight was shifted to MI gradually keeping SSVEP at less weights. Common spatial pattern (CSP) was used for MI and CCA for SSVEP classifications. This paradigm hypothesized that accurate feedback enhances MI.

The multiple channels hybrid was replaced in [66] combining SSVEP and MI in a single channel C3 or C4 improving performance. Seventeen healthy subjects (12 male and 5 female subjects with an average age around 23 years) were preinformed about the experiment to focus simultaneously on flickering visual stimuli of frequencies 15 and 20 Hz, and perform rightand left-hand MI, respectively. The average classification accuracy was around 85% for both channels.
