|عنوان فارسی مقاله:||سیستم ها و روش ها برای کنترل مبتنی بر SSVEP در دستگاه های الکتریکی|
|عنوان انگلیسی مقاله:||SYSTEM AND METHOD FOR SSVEP BASED CONTROL OF ELECTRICAL DEVICES|
|رشته های مرتبط:||مهندسی پزشکی، مهندسی برق، سایبرنتیک پزشکی و مهندسی کامپیوتر، هوش مصنوعی، الکترونیک قدرت و ماشینهای الکتریکی، مهندسی الکترونیک و مهندسی کنترل|
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Field 5 The present disclosure relates to brain – computer interface (BCI) techniques based upon steady state visual evoked potentials (SSVEPs). More particularly, aspects of the present disclosure relate to systems and methods for SSVEP based electrical device or appliance control that provide a simple electrode configuration for the capture of electroencephalographic (EEG) signals; a computationally efficient, accurate process by which EEG signals are analyzed and a 10 visual stimulus generator associated with an appliance identified; a simple, reliable multi-device power interface unit; and a simple, robust, assistance-free system activation process that avoids or eliminates visual fatigue and/or user distraction.
Background 15 Aspects of brain waves measured from the human scalp have been intensely researched as a result of efforts to develop brain-computer interface (BCI) systems and devices. A BCI is a direct communication pathway between a brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. BCI techniques play a prominent role in the development of systems that utilize 20 electromyogram (EMG), electrocorticogram (ECoG), or electroencephalogram (EEG) signals to facilitate a disabled user’s control of a neuroprosthetic device. EEG is a common non-invasive modality that can be used with persons with serious disability. EEG signals arise from electrical activity that can be detected external to the human scalp. EEG signals are produced by neural firing within the brain, and reflect correlated synaptic activity caused by post-synaptic 25 potentials generated by thousands or millions of cortical neurons having similar spatial orientation. Acquisition of EEG signals involves scalp electrodes or leads, typically using locations specified by the International 10-20 system. In EEG, minute potentials evoked by sensory stimuli are of particular importance as these time-locked transient wavelets show how 30 populations of cells behave in response to afferent volleys carried by primary sensory fibers. When a brief stimulus is presented to a subject, a transient brain response to that stimulation occurs. In general, EEG-based neuroprosthetic systems consist of a signal acquisition system, signal processing algorithms and application devices. Two modalities widely used in EEG-based neuroprosthetic systems are spontaneous EEG and event related potentials (ERPs) such as visual evoked potentials (VEPs). An evoked potential indicates the effect of a stimulus on the 5 brain, and is sensitive to changes in sensory and perceptual processes. A primary advantage of the VEP technique is its temporal resolution, which is limited only by measurement device sampling rate. VEPs can be categorized into transient visual evoked potentials (TVEPs) and steady state visual evoked potentials (SSVEPs). The SSVEP is a periodic response to a visual stimulus modulated 10 at a frequency higher than 6 Hz, and can be recorded at scalp locations corresponding to the visual cortex. The visual stimulus can be generated by a light emitting diode (LED) or a checkerboard or other pattern displayed by a liquid crystal display (LCD) screen. The SSVEP has the same fundamental frequency as that of the visual stimulus as well as its harmonics. In SSVEP-based systems, several stimuli coded by different frequencies are presented in the field 15 of vision and different SSVEP responses can be produced by shifting a user’s interest or attention to one of a number of frequency-coded stimuli. Prior techniques directed to stimulus selection fail to produce reliable SSVEP signals for accurate operation of EEG-based neuroprosthetic systems without incurring visual fatigue in the subject under consideration. Further, prior stimulus systems can require unnecessarily complex 20 circuitry, leading to increased system overhead and/or cost. Prior electrode configurations are also unnecessarily complex. For instance, in order to obtain reliable SSVEP signals, electrodes have been positioned at as many as 64 different scalp locations, resulting in increased cost and undesirably long signal processing times. Several attempts have been made to place fewer electrodes on the human scalp to obtain SSVEP signals, 25 but such attempts have led to poor and inaccurate SSVEP signals, and hence poor, inaccurate, and unreliable neuroprosthectic device control. Another problem arises from existing algorithms used to process the SSVEP signals. Current algorithms are complex and produce inaccurate or inconsistent results, and lack the capability to effectively discriminate similar stimuli which are near or next to each other. Yet another 30 problem arises because current implementations of EEG-based neuroprosthetic systems lack an easy to use, flexible, reliable and cost effective way of controlling multiple devices in response to EEG signals.Because EEG-based neuroprosthetic systems offer the potential to provide a significant positive impact upon physically challenged individuals’ lives, a need exists for improvement to existing EEG-based neuroprosthetic systems. It is therefore desirable to provide a solution to address at least one of the foregoing problems associated with EEG-based neuroprosthetic systems.