Not All Created Equal: Individual-Technology Fit of Brain-Computer Interfaces

Randolph, Adriane B.

doi:10.1109/hicss.2012.451

Cited by 36 publications

(35 citation statements)

References 18 publications

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“…Psychophysiological factors, such as attention and memory load, could contribute to the observed inter-subject variability. It has also been suggested that females, individuals over the age of 25, and those who play instruments are likely to perform better at mental imagery tasks (Randolph, 2012;Ahn and Jun, 2015). In this study, there was an imbalance between males and females; however, the total number of participants was not sufficient to assess if sex or age could have affected task performance.…”

Section: Discussionmentioning

confidence: 69%

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

et al. 2020

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Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional nearinfrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean timeof-flight of photons was used to calculate the change in concentrations of oxy-and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

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Section: Discussionmentioning

confidence: 69%

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

et al. 2020

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“…In particular, recent works have shown that individual users' characteristics, such as psychosocial and physiological parameters (e.g., gender, instrument playing, fine motor skills) or brain structures, can predict control performances for Mu-rhythm based BCI (Blankertz et al, 2010; Halder et al, 2011, 2013; Hammer et al, 2012; Randolph, 2012). …”

Section: State-of-the-artmentioning

confidence: 99%

“…[see, e.g., Larrue et al (2012) where users' characteristics where controlled in a study comparing navigation in VR with a BCI and with a treadmill]. A few studies have found correlations between psychological parameters and SMR-BCI control performances (Hammer et al, 2012; Randolph, 2012), which would suggest that matching users' characteristics to the corresponding BCI type is likely to optimize control performances. Similarly, matching training protocols to users' characteristics may make BCI training more efficient.…”

Section: Flaws In Bci Training Protocolsmentioning

confidence: 99%

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

Lotte¹,

Larrue²,

Mühl³

2013

Front. Hum. Neurosci.

239

259

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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.

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“…Motor imagery is defined as the mental simulation of a kinesthetic movement (Decety and Inqvar, 1990;Neuper et al, 2005). Signal processing algorithms, individual users' characteristics, such as psychosocial and physiological parameters (e.g., fine motor skills) or brain structures, can predict performances for SMR-based BCIs Halder et al, 2011Halder et al, , 2013Hammer et al, 2011;Randolph, 2012). Besides these factors, feedback is a necessary feature for initial learning of the BCI skill (Brown, 1970;Kuhlman, 1978;McFarland et al, 1998;Wolpaw et al, 1991Wolpaw et al, , 2002.…”

Section: Introductionmentioning

confidence: 99%