Pyff – A Pythonic Framework for Feedback Applications and Stimulus Presentation in Neuroscience

Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.

The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent object, its features have to be bound together. This was found to be achieved in cat and monkey brains by temporal correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30-80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscillations at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phenomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.

The capacity to deliberately control attention in order to hold and manipulate information in working memory is critical to higher cognitive functions. This suggests that between-subject differences in general cognitive ability might be related to observable differences in the activity of brain systems that support working memory and attention control. To test this notion, electroencephalograms were recorded from 80 healthy young adults during spatial working memory tasks. Measures of task-related neurophysiological and behavioral variables were derived from these data and compared to scores on a test battery commonly used to assess general cognitive ability (the WAIS-R). Subjects who scored high on the psychometric test also tended to respond faster in the experimental tasks without any loss of accuracy. The amplitude of the late positive component of the event-related potential was larger in high-ability subjects, and the frontal midline theta component of the EEG signal was also selectively enhanced in this group under conditions of sustained performance and high working memory load. These results suggest that subjects who scored high on the WAIS-R were better able to focus and sustain attention to task performance. Changes in the EEG alpha rhythm in response to manipulations of task practice and load were also examined and compared between frontal and parietal regions. The results indicated that high-ability subjects developed strategies that made relatively greater use of parietal regions, whereas low-ability subjects relied more exclusively on frontal regions. Other analyses indicated that hemispheric asymmetries in alpha band measures distinguish between individuals with relatively high verbal aptitude and those with relatively high nonverbal aptitude. In particular, subjects with a verbal cognitive style tended to make greater use of the left parietal region during task performance, and subjects with a nonverbal style tended to make greater use of the right parietal region. These results help clarify relationships between task-related brain activity and individual differences in cognitive ability and style.