Whether it's our location, contact lists, calendars, photo albums, or search requests, app developers, advertising companies, and other tech firms are scrambling to learn everything they can about us in order to sell us things.
The most commonly used technique is electroencephalography, which is widely known as a medical diagnostic test (especially for detecting seizures) but now has more potential uses. An EEG device is typically a headset with a small number of electrodes placed on different parts of the skull in order to detect the electrical signals made by your brainwaves. While EEGs cannot read your mind in the traditional, Professor X-y sense, it turns out that your brainwaves can reveal a great deal about you, such as your attention level and emotional state, and possibly much more. For instance, the presence of beta waves correlates with excitement, focus, and stress. One brain signal, known as the P300 response, correlates with recognition, say of a familiar face or object. This response is so well documented that it is widely used by psychologists and researchers in clinical studies. The popularity of EEG devices over other brain scanning technologies, like fMRIs, stems from their low cost, their light weight, and their ability to collect real-time data.
The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.
The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.
For the study, reported in the Journal of Neurophysiology.Contreras-Vidal successfully used EEG brain signals to reconstruct the complex 3-D movements of the ankle, knee, and hip joints during human treadmill walking. Two earlier studies showed (1) similar results for 3-D hand movement and (2) that subjects wearing the brain cap could control a computer cursor with their thoughts.
Scientists from the Universities of Oxford, California, and Geneva have shown they can discover secrets such as passwords and pin numbers using off the shelf technology, like the gaming headset from Emotiv, pictured here.
TOBI is a large European integrated project which will develop practical technology for brain-computer interaction (BCI) that will improve the quality of life of disabled people and the effectiveness of rehabilitation.
Michael Chorost is a man who has benefited from a brain–computer interface, though the kind of BCI implanted in his head after he went deaf in 2001, a cochlear implant, was not inserted directly into his brain, but into each of his inner ears. The result, after a lifetime of first being hard of hearing and then shut in complete auditory solitude, as he recounted in his memoir, Rebuilt: How Becoming Part Computer Made Me More Human (2005), was dramatic and life-changing. As his new, oddly jejune book, World Wide Mind: The Coming Integration of Humanity, Machines, and the Internet, makes clear, he is now a cheerleader for the rest of us getting kitted out with our own, truly personal, in-brain computers. In Chorost’s ideal world, which he lays out with the unequivocal zeal of a convert, we will all be connected directly to the Internet via a neural implant, so that the Internet “would become seamlessly part of us, as natural and simple to use as our own hands.”
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