Avatar navigation in Second Life using brain signals

Hazrati, Mehrnaz Kh.; Hofmann, Ulrich G.

doi:10.1109/wisp.2013.6657473

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…Dry Electrode Space Connection (Muñoz et al, 2016) Boot Camp Mod (Stein et al, 2018) Mental War (Vasiljevic et al, 2018) Avatar Navigation (Hazrati & Hofmann, 2013) Mind battlefield (Khong et al, 2014) Alpha wow (Van De Laar et al, 2013)…”

Section: Remote Testingmentioning

confidence: 99%

Mind Games: A Web-Based Multiplayer Brain-Computer Interface Game

Louis

Alikhademi

Joseph

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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The advancement of consumer-grade Brain-Computer Interface (BCI) devices has led to the use of brain wave data in disciplines beyond neuroscience. While BCI devices are studied holistically in some domains, the potential remains unexplored in other applicable domains, such as gaming. The majority of BCI games are single-player, only work on a single platform, or require expensive hardware that is difficult to use. To address these challenges, we propose Mind Games: a competitive multiplayer tug-of-war game played entirely with brain wave data in a web browser with a low-cost consumer-grade BCI device. Unlike other BCI games, Mind Games is platform-independent, has a quick setup time, and uses the latest web-based technologies: web-Bluetooth and JavaScript frameworks for networking and signal processing. This new cross-platform BCI pipeline enables physiological computing applications to be more accessible, leading the future of online multiplayer BCI games.

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Section: Remote Testingmentioning

confidence: 99%

Mind Games: A Web-Based Multiplayer Brain-Computer Interface Game

Louis

Alikhademi

Joseph

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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“…However, the benefits of using MI of the lower limbs during gait have not been as widely shown as they have been for upper limb movements, partly because of the complexity of the neural and biomechanical control of gait [32]. Hence, several studies have used MI of upper limb movements in a BCI to control the feedback of the navigation or lower limbs of an avatar [33][34][35][36][37][38] or of an exoskeleton [37]. For example, Hazrati and Hofmann [38] used the signals of MI of right/left hand movements to control the left/right navigation of an avatar.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, several studies have used MI of upper limb movements in a BCI to control the feedback of the navigation or lower limbs of an avatar [33][34][35][36][37][38] or of an exoskeleton [37]. For example, Hazrati and Hofmann [38] used the signals of MI of right/left hand movements to control the left/right navigation of an avatar. The same BCI with the same classification paradigm and mapping was used in [39], but this time with the additional possibility of self-paced navigation of the avatar.…”

Section: Introductionmentioning

confidence: 99%

A multi-modal modified feedback self-paced BCI to control the gait of an avatar

2021

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Brain–computer interfaces (BCIs) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. Some limitations of existing systems are: (a) some systems use mental imagery (MI) of movements other than gait; (b) most systems allow the user to take single steps or to walk but do not allow both; (c) most function in a single BCI mode (cue-paced or self-paced). Objective. The objective of this study was to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. Approach. This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this study, which was comprised of four sessions across four different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated regularized linear discriminate analysis classifiers that used the µ power spectral density over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback (MDF) was presented to half of the participants, where the avatar moved correctly regardless of the classification of the participants’ MI. The performance of the BCI was computed on each day, using different control modes. Main results. All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9 ± 8.4% showing that MDF enhanced BCI performance (p = 0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode. Significance. This study reports on a first BCI to use motor imagery of the lower limbs in order to control the gait of an avatar with different control modes and different control commands (single steps or forward walking). BCI performance is increased in a novel way by combining three different performance enhancement techniques, resulting in a single high performance and multi-modal BCI system. This study also showed that the improvements due to the effects of MDF lasted for more than one session.

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