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

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.

Effects of Different Re-referencing Methods on Spontaneously Generated Ear-EEG

“…Most ear-EEG-based BCIs have been developed based on exogenous paradigms that use EEGs evoked by external stimuli, such as auditory steady-state response (ASSR) (Kidmose et al, 2012, 2013a,b; Looney et al, 2012; Mikkelsen et al, 2015; Norton et al, 2015; Goverdovsky et al, 2016, 2017; Bech Christensen et al, 2017), steady-state visual evoked potential (SSVEP) (Kidmose et al, 2013b; Goverdovsky et al, 2016, 2017), and event-related potential (ERP) (Kidmose et al, 2012, 2013b; Bleichner et al, 2015, 2016; Debener et al, 2015; Norton et al, 2015; Fiedler et al, 2016, 2017; Pacharra et al, 2017). In our recent study (Choi et al, 2018), we verified the feasibility of using ear-EEG to realize an endogenous BCI using two mental tasks [mental arithmetic (MA) vs. mental singing (MS)] that induce high and low cognitive load, respectively. The two mental tasks that induce cognitive workload have been widely used in EEG-based BCI studies (Shin et al, 2016, 2017; So et al, 2017; Choi et al, 2018).…”

“…In our recent study (Choi et al, 2018), we verified the feasibility of using ear-EEG to realize an endogenous BCI using two mental tasks [mental arithmetic (MA) vs. mental singing (MS)] that induce high and low cognitive load, respectively. The two mental tasks that induce cognitive workload have been widely used in EEG-based BCI studies (Shin et al, 2016, 2017; So et al, 2017; Choi et al, 2018). Besides the development of BCI applications, ear-EEG has also been used to develop other brain applications, such as seizure detection (Do Valle et al, 2014; Gu et al, 2017; Zibrandtsen et al, 2017, 2018), sleep detection (Zibrandtsen et al, 2016), and brain authentication (Nakamura et al, 2018).…”

“…Two different types of ear-EEG systems have been introduced, depending on from where EEG is measured: (i) inside the ears (Looney et al, 2011, 2012; Kidmose et al, 2012, 2013a,b; Mikkelsen et al, 2015; Goverdovsky et al, 2016, 2017; Kappel et al, 2017; Zibrandtsen et al, 2017; Hong et al, 2018; Nakamura et al, 2018) and (ii) behind the ears (Debener et al, 2015; Bleichner et al, 2016; Goverdovsky et al, 2016; Mirkovic et al, 2016; Bleichner and Debener, 2017; Pacharra et al, 2017). The feasibility of ear-EEG to develop brain applications has been verified in terms of set-up time, performance, and long-term use; its set-up time is within several minutes (Looney et al, 2012; Bleichner et al, 2015, 2016; Debener et al, 2015; Goverdovsky et al, 2016; Zibrandtsen et al, 2016), its performance is comparable to that of conventional scalp-EEG (Kidmose et al, 2012, 2013b; Bleichner et al, 2015, 2016; Mikkelsen et al, 2015; Goverdovsky et al, 2016; Mirkovic et al, 2016; Zibrandtsen et al, 2016, 2017; Kappel et al, 2017; Pacharra et al, 2017; Choi et al, 2018), and its performance is maintained across multiple days (Norton et al, 2015). In early studies based on ear-EEG, resting-state EEG has been used to demonstrate its feasibility, where changes in alpha activity were investigated during eyes-closed (EC) and eyes-open (EO) conditions (Looney et al, 2011, 2012; Debener et al, 2015; Mikkelsen et al, 2015; Norton et al, 2015; Bleichner and Debener, 2017; Goverdovsky et al, 2017).…”

“…In early studies based on ear-EEG, resting-state EEG has been used to demonstrate its feasibility, where changes in alpha activity were investigated during eyes-closed (EC) and eyes-open (EO) conditions (Looney et al, 2011, 2012; Debener et al, 2015; Mikkelsen et al, 2015; Norton et al, 2015; Bleichner and Debener, 2017; Goverdovsky et al, 2017). One of the primary applications developed based on ear-EEG is brain-computer interface (BCI) (Bleichner et al, 2015, 2016; Debener et al, 2015; Norton et al, 2015; Fiedler et al, 2016, 2017; Mirkovic et al, 2016; Choi et al, 2018; Floriano et al, 2018; Wei et al, 2018), which is a communication channel operated by brain activity for paralyzed patients. Most ear-EEG-based BCIs have been developed based on exogenous paradigms that use EEGs evoked by external stimuli, such as auditory steady-state response (ASSR) (Kidmose et al, 2012, 2013a,b; Looney et al, 2012; Mikkelsen et al, 2015; Norton et al, 2015; Goverdovsky et al, 2016, 2017; Bech Christensen et al, 2017), steady-state visual evoked potential (SSVEP) (Kidmose et al, 2013b; Goverdovsky et al, 2016, 2017), and event-related potential (ERP) (Kidmose et al, 2012, 2013b; Bleichner et al, 2015, 2016; Debener et al, 2015; Norton et al, 2015; Fiedler et al, 2016, 2017; Pacharra et al, 2017).…”

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces

“…The BCI system performance is also affected by the signal quality, and different signal processing and pattern recognition techniques are used [20][21][22]. Several studies have investigated and compared different signal processing and pattern recognition techniques, and some studies have investigated the signal quality and BCI performance of different headsets or electrode types (dry vs. wet) [16,18,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. The focus of these studies has primarily been on BCI control signals related to communication or control, such as P300 or steady-state visual evoked potentials (SSVEP).…”