Ali Bahramisharif

Covert attention allows for continuous control of brain-computer interfaces

While brain‐computer interfaces (BCIs) can be used for controlling external devices, they also hold the promise of providing a new tool for studying the working brain. In this study we investigated whether modulations of brain activity by changes in covert attention can be used as a continuous control signal for BCI. Covert attention is the act of mentally focusing on a peripheral sensory stimulus without changing gaze direction. The ongoing brain activity was recorded using magnetoencephalography in subjects as they covertly attended to a moving cue while maintaining fixation. Based on posterior alpha power alone, the direction to which subjects were attending could be recovered using circular regression. Results show that the angle of attention could be predicted with a mean absolute deviation of 51° in our best subject. Averaged over subjects, the mean deviation was ∼70°. In terms of information transfer rate, the optimal data length used for recovering the direction of attention was found to be 1700 ms; this resulted in a mean absolute deviation of 60° for the best subject. The results were obtained without any subject‐specific feature selection and did not require prior subject training. Our findings demonstrate that modulations of posterior alpha activity due to the direction of covert attention has potential as a control signal for continuous control in a BCI setting. Our approach will have several applications, including a brain‐controlled computer mouse and improved methods for neuro‐feedback that allow direct training of subjects’ ability to modulate posterior alpha activity.

Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention.

BackgroundVisual brain-computer interfaces (BCIs) often yield high performance only when targets are fixated with the eyes. Furthermore, many paradigms use intense visual stimulation, which can be irritating especially in long BCI sessions. However, BCIs can more directly directly tap the neural processes underlying visual attention. Covert shifts of visual attention induce changes in oscillatory alpha activity in posterior cortex, even in the absence of visual stimulation. The aim was to investigate whether different pairs of directions of attention shifts can be reliably differentiated based on the electroencephalogram. To this end, healthy participants (N = 8) had to strictly fixate a central dot and covertly shift visual attention to one out of six cued directions.ResultsCovert attention shifts induced a prolonged alpha synchronization over posterior electrode sites (PO and O electrodes). Spectral changes had specific topographies so that different pairs of directions could be differentiated. There was substantial variation across participants with respect to the direction pairs that could be reliably classified. Mean accuracy for the best-classifiable pair amounted to 74.6%. Furthermore, an alpha power index obtained during a relaxation measurement showed to be predictive of peak BCI performance (r = .66).ConclusionsResults confirm posterior alpha power modulations as a viable input modality for gaze-independent EEG-based BCIs. The pair of directions yielding optimal performance varies across participants. Consequently, participants with low control for standard directions such as left-right might resort to other pairs of directions including top and bottom. Additionally, a simple alpha index was shown to predict prospective BCI performance.

2009 Special Issue: Selecting features for BCI control based on a covert spatial attention paradigm

Covert attention to spatial locations in the visual field is a relatively new control signal for brain-computer interfaces. Previous EEG research has shown that trials can be classified by thresholding based on left and right hemisphere alpha power in covert spatial attention paradigms. We reexamine the covert attention paradigm based on MEG measurements for fifteen subjects. It is shown that classification performance can be improved by applying sparse logistic regression in order to select a subset of the sensors specific to each subject as the basis for classification. Furthermore, insight is gained into how classification performance changes as a function of the length of the attention period and as a function of the number of trials. Classification performance steadily increases as the length of the attention period over which is averaged is increased, although this does not necessarily translate into higher bit rates. Good classification performance using early components of the attention period may be related to evoked response. With regard to the number of used trials, classification performance became maximal after 150 samples had been obtained, requiring a training time of approximately eleven minutes under the current experimental paradigm.

Measuring directionality between neuronal oscillations of different frequencies

It is well established that neuronal oscillations at different frequencies interact with each other in terms of cross-frequency coupling (CFC). In particular, the phase of slower oscillations modulates the power of faster oscillations. This is referred to as phase-amplitude coupling (PAC). Examples are alpha phase to gamma power coupling as observed in humans and theta phase to gamma power coupling as observed in the rat hippocampus. We here ask if the interaction between alpha and gamma oscillations is in the direction of the phase of slower oscillations driving the power of faster oscillations or conversely from the power of faster oscillations driving the phase of slower oscillations. To answer this question, we introduce a new measure to estimate the cross-frequency directionality (CFD). This measure is based on the phase-slope index (PSI) between the phase of slower oscillations and the power envelope of faster oscillations. Further, we propose a randomization framework for statistically evaluating the coupling measures when controlling for multiple comparisons over the investigated frequency ranges. The method was firstly validated on simulated data and next applied to resting state electrocorticography (ECoG) data. These results demonstrate that the method works reliably. In particular, we found that the power envelope of gamma oscillations drives the phase of slower oscillations in the alpha band. This surprising finding is not easily reconcilable with theories suggesting that feedback controlled alpha oscillations modulate feedforward processing reflected in the gamma band.

Propagating Neocortical Gamma Bursts Are Coordinated by Traveling Alpha Waves

Neocortical neuronal activity is characterized by complex spatiotemporal dynamics. Although slow oscillations have been shown to travel over space in terms of consistent phase advances, it is unknown how this phenomenon relates to neuronal activity in other frequency bands. We here present electrocorticographic data from three male and one female human subject and demonstrate that gamma power is phase locked to traveling alpha waves. Given that alpha activity has been proposed to coordinate neuronal processing reflected in the gamma band, we suggest that alpha waves are involved in coordinating neuronal processing in both space and time.

Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience.

Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain–computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

MEG-based decoding of the spatiotemporal dynamics of visual category perception

Visual processing is a complex task which is best investigated using sensitive multivariate analysis methods that can capture representation-specific brain activity over both time and space. In this study, we applied a multivariate decoding algorithm to MEG data of subjects engaged in passive viewing of images of faces, scenes, bodies and tools. We used reconstructed source-space time courses as input to the algorithm in order to localize brain regions involved in optimal image discrimination. Applying this method to the interval of 115 to 315 ms after stimulus onset, we show a focal localization of regression coefficients in the inferior occipital, middle occipital, and lingual gyrus that drive decoding of the different perceived image categories. Classifier accuracy was highest (over 90% correctly classified trials, compared to a chance level accuracy of 50%) when dissociating the perception of faces from perception of other object categories. Furthermore, we applied this method to each single time point to extract the temporal evolution of visual perception. This allowed for the detection of differences in visual category perception as early as 85 ms after stimulus onset. Furthermore, localizing the corresponding regression coefficients of each time point allowed us to capture the spatiotemporal dynamics of visual category perception. This revealed initial involvement of sources in the inferior occipital, inferior temporal and superior occipital gyrus. During sustained stimulation additional sources in the anterior inferior temporal gyrus and superior parietal gyrus became involved. We conclude that decoding of source-space MEG data provides a suitable method to investigate the spatiotemporal dynamics of ongoing cognitive processing.

Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations

Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.

Lateralized responses during covert attention are modulated by target eccentricity

Various studies have demonstrated that covert attention to different locations in the visual field can be used as a control signal for brain computer interfacing. It is well known that when covert attention is directed to the left visual hemifield, posterior alpha activity decreases in the right hemisphere while simultaneously increasing in the left hemisphere and vice versa. However, it remains unknown if and how the classical lateralization pattern depends on the eccentricity of the locations to which one attends. In this paper we study the effect of target eccentricity on the performance of a brain computer interface system that is driven by covert attention. Results show that the lateralization pattern becomes more pronounced as target eccentricity increases and suggest that in the current design the minimum eccentricity for having an acceptable classification performance for two targets at equal distance from fixation in opposite hemifields is about 6° of visual angle.

Serial representation of items during working memory maintenance at letter-selective cortical sites

A key component of working memory is the ability to remember multiple items simultaneously. To understand how the human brain maintains multiple items in memory, we examined direct brain recordings of neural oscillations from neurosurgical patients as they performed a working memory task. We analyzed the data to identify the neural representations of individual memory items by identifying recording sites with broadband gamma activity that varied according to the identity of the letter a subject viewed. Next, we tested a previously proposed model of working memory, which had hypothesized that the neural representations of individual memory items sequentially occurred at different phases of the theta/alpha cycle. Consistent with this model, the phase of the theta/alpha oscillation when stimulus-related gamma activity occurred during maintenance reflected the order of list presentation. These results suggest that working memory is organized by a cortical phase code coordinated by coupled theta/alpha and gamma oscillations and, more broadly, provide support for the serial representation of items in working memory.