Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

Abstract

While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.

The effect of multimodal and enriched feedback on SMR-BCI performance

“…This is in accordance with Schreuder et al (2010) who also found no effect of multimodal (auditory and visual) feedback on performance with a BCI using slow cortical potentials as input signal. An efficient feedback should not be too complex, and should be provided in manageable pieces (Lotte et al, 2013). It may be that the visual feedback was too dominant such that the simultaneous auditory feedback did not provide any beneficial information.…”

“…They enhanced the discrimination of the motor imagery classes which made the system more robust against potential changes in the environment. Besides online, or even offline adaptation in the classifier, other factors like training, new task instructions and feedback (Allison and Neuper, 2010;Lotte et al, 2013;Pfurtscheller et al, 2006Pfurtscheller et al, , 2007 can also play an important role in learning to control a BCI. We decided to train end-user with a non-adaptive classifier to focus on the potential effect of an enriched feedback and to be able to exclude any other factors besides the type of feedback.…”

“…Although the most common feedback is visual, there is evidence that training can be enhanced by providing multimodal feedback with the same granularity and specificity for each modality (Ainsworth, 2006, Lotte et al, 2013. Kaufman et al (2011) provided their BCI users with a cursor indicating the integrated classifier output, and the instantaneous sign and absolute value, coded as the colour and intensity of the cursor.…”

Brain-Controlled Biometric Signals Employed to Operate External Technical Devices

“…The control of any type of BCI required preliminary training, which included both the procedure of BCI pilot obtaining the skill to control the interface and the training of the data processing algorithm with the data from the training recording [15]. This gave us two major fields of potential improvement: first, different practices can be used to improve the learning process of the user.…”

“…Such control strategy will theoretical have almost unlimited computational power. For example, simple brain-controlled grasping movement of a finger (10 15 possible combinations of muscle contraction) can be estimated as 10 6 GHz computer equivalent to choose a correct muscle contraction template in real time [11]. Brain mechanisms of such control remain largely unknown.…”