Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain–computer interface

Abstract

Event-related desynchronization (ERD) of the electroencephalogram (EEG) from the motor cortex is associated with execution, observation, and mental imagery of motor tasks. Generation of ERD by motor imagery (MI) has been widely used for brain-computer interfaces (BCIs) linked to neuroprosthetics and other motor assistance devices. Control of MI-based BCIs can be acquired by neurofeedback training to reliably induce MI-associated ERD. To develop more effective training conditions, we investigated the effect of static and dynamic visual representations of target movements (a picture of forearms or a video clip of hand grasping movements) during the BCI neurofeedback training. After 4 consecutive training days, the group that performed MI while viewing the video showed significant improvement in generating MI-associated ERD compared with the group that viewed the static image. This result suggests that passively observing the target movement during MI would improve the associated mental imagery and enhance MI-based BCIs skills.

Combined Action Observation and Motor Imagery Neurofeedback for Modulation of Brain Activity

“…This is an unexpected result given previous findings that typically show a reduction in mu rhythm magnitude during tasks involving sensorimotor processes (Nedelko et al, 2012; Villiger et al, 2013; Kondo et al, 2015). Several studies of II have found it to be associated with increased motor cortex excitability (Sakamoto et al, 2009; Ohno et al, 2011; Tsukazaki et al, 2012; Wright et al, 2014), and increased activation of the motor network more broadly (Nedelko et al, 2012; Villiger et al, 2013), when compared to MI or AO alone.…”

“…The majority of studies investigating motor simulation utilize short (<10 s), discrete blocks of MI, AO, or II, and thus it is possible that the difficulty inherent in producing ERD for such an extended period lead to the higher log2 mu ERD/S values we see in the NFB task (see Figures 4A,B). Indeed, the only other NFB system that utilized II had subjects perform 4 s blocks of II (Kondo et al, 2015). While this is a limitation of the current study, clinical studies have shown that MI-NFB, used in conjunction with traditional physiotherapy, can improve patient's clinical outcomes, despite the fact that the patient's ability to gain control of the NFB system was not statistically significant (Ramos-Murguialday et al, 2013; Pichiorri et al, 2015).…”

“…Recently, researchers have used a combined MI/AO approach: here an individual watches a motor task being performed repetitively, while simultaneously imagining they are performing the movement themselves. This approach of “Imagined Imitation” (II) has been shown to facilitate corticospinal excitability to a greater degree than either AO or MI alone (Sakamoto et al, 2009; Ohno et al, 2011; Tsukazaki et al, 2012; Wright et al, 2014), and to increase brain activity in several regions critical for motor learning and performance over and above that seen in AO or MI (Macuga and Frey, 2012; Nedelko et al, 2012; Villiger et al, 2013; Kondo et al, 2015). …”

“…Given the ability of II to engage the sensorimotor system to a greater degree than MI or AO alone, it is possible that the use of II-NFB as an adjunct therapy in stroke rehabilitation could provide greater benefits than those demonstrated by the use of MI-NFB (Mihara et al, 2013; Ramos-Murguialday et al, 2013; Pichiorri et al, 2015). To date, II-NFB has only been attempted in one study (Kondo et al, 2015), where subjects performed short (4 s) blocks of II or MI (accompanied by a static image), then saw a binary indication of success during a rest period. While this represents an interesting and novel step forward, a real-time II-NFB system has yet to be tested; such a system would be of note as it circumvents a central limitation of current MI-neurofeedback systems: the fact that imagery is best accomplished with the eyes closed limits designers, making the delivery of visual, real-time feedback suboptimal.…”

Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment

“…Cho and Lee [50] implemented a real-time game environment system using game player's emotional state information from the BCI sensor to raise the degree of immersion in an FPS game. Kondo and colleagues [51] investigated the effect of static and dynamic visual representations of target movements during BCI neurofeedback training, which revealed that dynamic images showed significant improvement in generating MI-associated ERD compared with static images. Belkacem et al [52] presented real-time control of a video game with eye movements for an asynchronous and noninvasive communication system using two temporal EEG sensors.…”