Tagged: Humans

Brain-computer interfaces

The human brain is perhaps the most fascinating and complex signal processing machine in existence. It is capable of transducing a variety of environmental signals (the senses, including taste, touch, smell, sound, and sight) and extracting information from these disparate signal streams, ultimately fusing this information to enable behavior, cognition, and action. What is perhaps surprising is that the basic signal processing elements of the brain, i.e., neurons, transmit information at a relatively slow rate compared to transistors, switching about 106 times slower in fact. The brain has the advantage of having a tremendous number of neurons, all operating in parallel, and a highly distributed memory system of synapses (over 100 trillion in the cerebral cortex) and thus its signal processing capabilities may largely arise from its unique architecture. These facts have inspired a great deal of study of the brain from a signal processing perspective. Recently, scientists and engineers have focused on developing means in which to directly interface with the brain, essentially measuring neural signals and decoding them to augment and emulate behavior. This research area has been termed brain computer interfaces and is the topic of this issue of IEEE Signal Processing Magazine.

Spatio-temporal linear discrimination for inferring task difficulty from EEG

We present a spatio-temporal linear discrimination method for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required i.e. the algorithm requires no knowledge about the timing and/or spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel and then spatially integrates the channels for each channel-specific onset time. The algorithm can be seen as learning discrimination trajectories defined within the space of EEG channels. We demonstrate the method for detecting auditory evoked neural activity and discrimination of task difficulty in a complex visual-auditory environment

Texture discrimination and binding by a modified energy model

The model presented shows how textured regions can be discriminated and textured surface created by the visual cortex. The model addresses two major processes: texture segmentation and texture binding. Textures are detected by using a version of the energy model of J. R. Bergen and E. H. Adelson (1988) and J. R. Bergen and M. S. Landy (1991), which was modified to include ON and OFF center cells, and units selective for line endings. A novel neural mechanism is described for binding a texture pattern together. Simulation results demonstrated the ability of the networks to segment and bind a well-known texture pattern.