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Clinical Trial
. 2018 Aug 29;18(9):2856.
doi: 10.3390/s18092856.

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Soo-In Choi  1 , Chang-Hee Han  2 , Ga-Young Choi  3 , Jaeyoung Shin  4 , Kwang Soup Song  5 , Chang-Hwan Im  6 , Han-Jeong Hwang  7
Affiliations
Clinical Trial

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Soo-In Choi et al. Sensors (Basel). .

Abstract

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

Keywords: brain-computer interface (BCI); ear-EEG; electroencephalography (EEG); endogenous BCI; mental arithmetic.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Electrode positions used to record EEG data. (A) The brain area is divided into four regions of interests (ROIs) for data analysis (frontal, central, occipital, and ear area). (B) The electrode placement for ear-EEG.
Figure 2
Figure 2
Experimental paradigm of one session used in the main experiment. In the beginning of each session, a rest period of 10 s is performed. The string ‘ABC’ and an asterisk are presented to indicate a rest period and the subject is asked to fix the eyes to the asterisk to minimize ocular movement. After the rest period, either mental arithmetic (MA) or light cognitive (LC) task is randomly performed. For MA, a pair of a three-digit number and a single-digit number between 5 and 9 is randomly presented, and the subject is asked to sequentially subtract the single-digit number from the three-digit number (e.g., 477 − 8) for 10 s. For LC, the string ‘ABC’ is presented, and the subject is asked to internally imagine vocalization of the English alphabet from A to Z with a 1 Hz speed for 10 s. Both MA and LC are performed ten times in each session, and each subject completes five sessions (50 MA and 50 LC in total). A short beep (300 ms) is presented at every screen transition (red speaker icons).
Figure 3
Figure 3
Grand average time-frequency maps with eyes closed (EC) and eyes opened (EO) for (A) frontal, (B) central, (C) occipital, and (D) ear area. The color scale was chosen to fit the range for (D) ear area.
Figure 4
Figure 4
Grand average ERD/ERS maps of all electrodes during MA. The four regions of interest (ROIs), frontal, central, occipital, and ear area, are denoted by four different colored lines and titles for each map (green, orange, red, and gray), respectively. The x- and y-axis of each map indicate the task time from −2–10 s based on task onset (t = 0 s), and the frequency band ranging from 1 to 50 Hz, respectively. ERD and ERS are presented in blue and red, respectively. Note that scalp- and ear-EEG are independently re-referenced using a CAR and a modified CAR, respectively.
Figure 5
Figure 5
Grand average ERD/ERS maps of all electrodes during LC. The four regions of interest (ROIs), frontal, central, occipital, and ear area, are denoted by four different colored lines and titles for each map (green, orange, red, and gray), respectively. The x- and y-axis of each map indicate the task time from −2–10 s based on task onset (t = 0 s), and the frequency band ranging from 1 to 50 Hz, respectively. ERD and ERS are presented in blue and red, respectively. Note that scalp- and ear-EEG are independently re-referenced using a CAR and a modified CAR, respectively.
Figure 6
Figure 6
Grand average ERD/ERS maps of each ROI during MA and LC, and their differences (MA-LC). The ERD/ERS maps of ear area (denoted by ‘Ear’) are obtained by averaging the six electrodes attached behind both ears (three electrode for each side). The x- and y-axis of each map indicate the task time from −2–10 s based on task onset (t = 0 s), and the frequency band ranging from 1 to 50 Hz, respectively. ERD and ERS are presented in blue and red, respectively.
Figure 7
Figure 7
(A) Electrode positions used to create each ROI, and (B) the mean classification accuracies of the four ROIs with that obtained using all electrodes (‘Scalp’), excluding the six ear electrodes. Each ROI was individually re-referenced, where a CAR was used for the scalp ROIs (‘Scalp’, ‘Frontal’, ‘Central’, and ‘Occipital’) while the mean of three electrodes attached on an opposite ear area was used as a reference signal for ear ROI (‘Ear’). Error bars indicate standard deviations of the estimated classification accuracies of each ROI. There is no significant difference between the four ROIs (Friedman test; p = 0.63).
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