Dataset Information

Brain Machine Interfaces for Vision Restoration: The Current State of Cortical Visual Prosthetics.

ABSTRACT: Loss of vision alters the day to day life of blind individuals and may impose a significant burden on their family and the economy. Cortical visual prosthetics have been shown to have the potential of restoring a useful degree of vision via stimulation of primary visual cortex. Due to current advances in electrode design and wireless power and data transmission, development of these prosthetics has gained momentum in the past few years and multiple sites around the world are currently developing and testing their designs. In this review, we briefly outline the visual prosthetic approaches and describe the history of cortical visual prosthetics. Next, we focus on the state of the art of cortical visual prosthesis by briefly explaining the design of current devices that are either under development or in the clinical testing phase. Lastly, we shed light on the challenges of each design and provide some potential solutions.

SUBMITTER: Niketeghad S

PROVIDER: S-EPMC6361050 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Brain Machine Interfaces for Vision Restoration: The Current State of Cortical Visual Prosthetics.

Niketeghad Soroush S Pouratian Nader N

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 20190101 1

Loss of vision alters the day to day life of blind individuals and may impose a significant burden on their family and the economy. Cortical visual prosthetics have been shown to have the potential of restoring a useful degree of vision via stimulation of primary visual cortex. Due to current advances in electrode design and wireless power and data transmission, development of these prosthetics has gained momentum in the past few years and multiple sites around the world are currently developing ...[more]

PMID: 30194614

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Project description:In this study, we proposed a new type of hybrid visual stimuli for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which incorporate various periodic motions into conventional flickering stimuli (FS) or pattern reversal stimuli (PRS). Furthermore, we investigated optimal periodic motions for each FS and PRS to enhance the performance of SSVEP-based BCIs. Periodic motions were implemented by changing the size of the stimulus according to four different temporal functions denoted by none, square, triangular, and sine, yielding a total of eight hybrid visual stimuli. Additionally, we developed the extended version of filter bank canonical correlation analysis (FBCCA), which is a state-of-the-art training-free classification algorithm for SSVEP-based BCIs, to enhance the classification accuracy for PRS-based hybrid visual stimuli. Twenty healthy individuals participated in the SSVEP-based BCI experiment to discriminate four visual stimuli with different frequencies. An average classification accuracy and information transfer rate (ITR) were evaluated to compare the performances of SSVEP-based BCIs for different hybrid visual stimuli. Additionally, the user's visual fatigue for each of the hybrid visual stimuli was also evaluated. As the result, for FS, the highest performances were reported when the periodic motion of the sine waveform was incorporated for all window sizes except for 3 s. For PRS, the periodic motion of the square waveform showed the highest classification accuracies for all tested window sizes. A significant statistical difference in the performance between the two best stimuli was not observed. The averaged fatigue scores were reported to be 5.3 ± 2.05 and 4.05 ± 1.28 for FS with sine-wave periodic motion and PRS with square-wave periodic motion, respectively. Consequently, our results demonstrated that FS with sine-wave periodic motion and PRS with square-wave periodic motion could effectively improve the BCI performances compared to conventional FS and PRS. In addition, thanks to its low visual fatigue, PRS with square-wave periodic motion can be regarded as the most appropriate visual stimulus for the long-term use of SSVEP-based BCIs, particularly for window sizes equal to or larger than 2 s.

Dataset Information

Brain Machine Interfaces for Vision Restoration: The Current State of Cortical Visual Prosthetics.

Publications

Brain Machine Interfaces for Vision Restoration: The Current State of Cortical Visual Prosthetics.

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