Motivated by the success of cochlear implants for deaf patients, we are now facing the goal of creating a visual neuroprosthesis designed to interface with the occipital cortex as a means through which a limited but useful sense of vision could be restored in profoundly blind patients. We review the most important challenges regarding this neuroprosthetic approach and emphasize the need for basic human psychophysical research on the best way of presenting complex stimulating patterns through multiple microelectrodes. Continued research will hopefully lead to the development of and design specifications for the first generation of a cortically based visual prosthesis system.

Clinical applications such as artificial vision require extraordinary, diverse, lengthy and intimate collaborations among basic scientists, engineers and clinicians. In this review, we present the state of research on a visual neuroprosthesis designed to interface with the occipital visual cortex as a means through which a limited, but useful, visual sense could be restored in profoundly blind individuals. We review the most important physiological principles regarding this neuroprosthetic approach and emphasize the role of neural plasticity in order to achieve desired behavioral outcomes. While full restoration of fine detailed vision with current technology is unlikely in the immediate near future, the discrimination of shapes and the localization of objects should be possible allowing blind subjects to navigate in a unfamiliar environment and perhaps even to read enlarged text. Continued research and development in neuroprosthesis technology will likely result in a substantial improvement in the quality of life of blind and visually impaired individuals.

The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.

Brain–computer interfaces (BCIs) are mainly intended for people unable to perform any muscular movement, such as patients in a complete locked-in state. The majority of BCIs interact visually with the user, either in the form of stimulation or biofeedback. However, visual BCIs challenge their ultimate use because they require the subjects to gaze, explore and shift eye-gaze using their muscles, thus excluding patients in a complete locked-in state or under the condition of the unresponsive wakefulness syndrome. In this study, we present a novel fully auditory EEG-BCI based on a dichotic listening paradigm using human voice for stimulation. This interface has been evaluated with healthy volunteers, achieving an average information transmission rate of 1.5 bits min⁻¹ in full-length trials and 2.7 bits min⁻¹ using the optimal length of trials, recorded with only one channel and without formal training. This novel technique opens the door to a more natural communication with users unable to use visual BCIs, with promising results in terms of performance, usability, training and cognitive effort.

With the fast and unstoppable evolution of robotics and artificial intelligence, effective autonomous navigation in real-world scenarios has become one of the most pressing challenges in the literature. However, demanding requirements, such as real-time operation, energy and computational efficiency, robustness, and reliability, make most current solutions unsuitable for real-world challenges. Thus, researchers are fostered to seek innovative approaches, such as bio-inspired solutions. Indeed, animals have the intrinsic ability to efficiently perceive, understand, and navigate their unstructured surroundings. To do so, they exploit self-motion cues, proprioception, and visual flow in a cognitive process to map their environment and locate themselves within it. Computational neuroscientists aim to answer “how” and “why” such cognitive processes occur in the brain, to design novel neuromorphic sensors and methods that imitate biological processing. This survey aims to comprehensively review the application of brain-inspired strategies to autonomous navigation. The paper delves into areas such as neuromorphic perception, asynchronous event processing, energy-efficient and adaptive learning, and the emulation of brain regions vital for navigation, such as the hippocampus and entorhinal cortex.