Gary Garcia-Molina

Emotional brain-computer interfaces

Research in Brain-Computer Interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide significant insight into the users emotional state. This information can be utilized in two manners. 1) Knowledge of the influence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subjects emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates. These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of non-invasive EEG based BCIs.

Enhancement of sleep slow waves: underlying mechanisms and practical consequences.

Even modest sleep restriction, especially the loss of sleep slow wave activity (SWA), is invariably associated with slower electroencephalogram (EEG) activity during wake, the occurrence of local sleep in an otherwise awake brain, and impaired performance due to cognitive and memory deficits. Recent studies not only confirm the beneficial role of sleep in memory consolidation, but also point to a specific role for sleep slow waves. Thus, the implementation of methods to enhance sleep slow waves without unwanted arousals or lightening of sleep could have significant practical implications. Here we first review the evidence that it is possible to enhance sleep slow waves in humans using transcranial direct-current stimulation (tDCS) and transcranial magnetic stimulation. Since these methods are currently impractical and their safety is questionable, especially for chronic long-term exposure, we then discuss novel data suggesting that it is possible to enhance slow waves using sensory stimuli. We consider the physiology of the K-complex (KC), a peripheral evoked slow wave, and show that, among different sensory modalities, acoustic stimulation is the most effective in increasing the magnitude of slow waves, likely through the activation of non-lemniscal ascending pathways to the thalamo-cortical system. In addition, we discuss how intensity and frequency of the acoustic stimuli, as well as exact timing and pattern of stimulation, affect sleep enhancement. Finally, we discuss automated algorithms that read the EEG and, in real-time, adjust the stimulation parameters in a closed-loop manner to obtain an increase in sleep slow waves and avoid undesirable arousals. In conclusion, while discussing the mechanisms that underlie the generation of sleep slow waves, we review the converging evidence showing that acoustic stimulation is safe and represents an ideal tool for slow wave sleep (SWS) enhancement.

Optimal spatial filtering for the steady state visual evoked potential: BCI application

Focusing of attention on a repetitive visual stimulation (RVS) at a constant frequency, elicits the so called steady-state visual evoked potential (SSVEP). This effect can be advantageously utilized in brain-computer interfaces (BCIs). SSVEP based BCIs can offer higher bitrates and require shorter training time as compared to other BCI modalities. Detection of the SSVEP from the EEG can be facilitated through spatial filtering (linear combination of the signals recorded at several electrodes). Literature offers several options to perform this. In this paper we propose a taxonomy to categorize these methods and we extensively evaluate them using 22 stimulation frequencies. We suggest improvements to existing methods to increase the SSVEP detection performance. We also consider practical aspects in the discussion of results.

Online BCI implementation of high-frequency phase modulated visual stimuli

Brain computer interfaces (BCI) that use the steady-statevisual-evoked-potential (SSVEP) as neural source, offer two main advantages over other types of BCIs: shorter calibration times and higher information transfer rates. SSVEPs elicited by high frequency (larger than 30 Hz) repetitive visual stimulation are less prone to cause visual fatigue, safer, and more comfortable for the user. However in the high frequency range there is a practical limitation because only few frequencies can elicit sufficiently strong SSVEPs for BCI purposes. We bypass this limitation by using only one stimulation frequency and different phases. To detect the phase from the recorded SSVEP, we use spatial filtering combined to phase synchrony analysis. We developed an online BCI implementation which was tested on six subjects and resulted on an average accuracy of 95.5% and an average bit rate of 34 bits-per-minute. Our approach has the advantage of entailing only minimal visual annoyance for the user.

Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature sets is compared using the kappa statistic which has very recently become popular in sleep staging research. A variable duration of the EEG segment (or epoch) to decide on the sleep stage is also analyzed. Spectral-domain, time-domain, linear, and nonlinear features are compared in terms of performance and two types of machine learning approaches (random forests and support vector machines) are assessed. We have determined that frontal EEG signals, with spectral linear features, epoch durations between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation.

Dry and Water-Based EEG Electrodes in SSVEP-Based BCI Applications

This paper evaluates whether water-based and dry contact electrode solutions can replace the gel ones in measuring electrical brain activity by the electroencephalogram (EEG). The quality of the signals measured by three setups (dry, water, and gel), each using 8 electrodes, is estimated for the case of a brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP). Repetitive visual stimuli in the low (12 to 21Hz) and high (28 to 40Hz) frequency ranges were applied. Six people, that had different hair length and type, participated in the experiment. For people with shorter hair style the performance of water-based and dry electrodes comes close to the gel ones in the optimal setting. On average, the classification accuracy of 0.63 for dry and 0.88 for water-based electrodes is achieved, compared to the 0.96 obtained for gel electrodes. The theoretical maximum of the average information transfer rate across participants was 23bpm for dry, 38bpm for water-based and 67bpm for gel electrodes. Furthermore, the convenience level of all three setups was seen as comparable. These results demonstrate that, having optimized headset and electrode design, dry and water-based electrodes can replace gel ones in BCI applications where lower communication speed is acceptable.

Neural dynamics during repetitive visual stimulation

OBJECTIVE Steady-state visual evoked potentials (SSVEPs), the brain responses to repetitive visual stimulation (RVS), are widely utilized in neuroscience. Their high signal-to-noise ratio and ability to entrain oscillatory brain activity are beneficial for their applications in brain-computer interfaces, investigation of neural processes underlying brain rhythmic activity (steady-state topography) and probing the causal role of brain rhythms in cognition and emotion. This paper aims at analyzing the space and time EEG dynamics in response to RVS at the frequency of stimulation and ongoing rhythms in the delta, theta, alpha, beta, and gamma bands. APPROACH We used electroencephalography (EEG) to study the oscillatory brain dynamics during RVS at 10 frequencies in the gamma band (40-60 Hz). We collected an extensive EEG data set from 32 participants and analyzed the RVS evoked and induced responses in the time-frequency domain. MAIN RESULTS Stable SSVEP over parieto-occipital sites was observed at each of the fundamental frequencies and their harmonics and sub-harmonics. Both the strength and the spatial propagation of the SSVEP response seem sensitive to stimulus frequency. The SSVEP was more localized around the parieto-occipital sites for higher frequencies (>54 Hz) and spread to fronto-central locations for lower frequencies. We observed a strong negative correlation between stimulation frequency and relative power change at that frequency, the first harmonic and the sub-harmonic components over occipital sites. Interestingly, over parietal sites for sub-harmonics a positive correlation of relative power change and stimulation frequency was found. A number of distinct patterns in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (15-30 Hz) bands were also observed. The transient response, from 0 to about 300 ms after stimulation onset, was accompanied by increase in delta and theta power over fronto-central and occipital sites, which returned to baseline after approx. 500 ms. During the steady-state response, we observed alpha band desynchronization over occipital sites and after 500 ms also over frontal sites, while neighboring areas synchronized. The power in beta band over occipital sites increased during the stimulation period, possibly caused by increase in power at sub-harmonic frequencies of stimulation. Gamma power was also enhanced by the stimulation. SIGNIFICANCE These findings have direct implications on the use of RVS and SSVEPs for neural process investigation through steady-state topography, controlled entrainment of brain oscillations and BCIs. A deep understanding of SSVEP propagation in time and space and the link with ongoing brain rhythms is crucial for optimizing the typical SSVEP applications for studying, assisting, or augmenting human cognitive and sensorimotor function.

Eliciting steady state visual evoked potentials near the visual perception threshold

Steady state visual evoked potentials (SSVEP) are widely used in EEG research as they offer a relatively high signal to noise ratio allowing the investigation of visual processing at the cortical level. Presentation of a repetitive visual stimulus (flicker, RVS) at a frequency in the range from approximately 1 to 100 Hz, elicits an oscillatory response at the same frequency of the stimulus and/or harmonics which can be observed in the electroencephalogram (EEG), particulary at occipital sites. The frequency and the modulation depth of the RVS determine the strength of the corresponding SSVEP response. While a stronger response allows for a higher signal-to-noise ratio, it does also negatively influence comfort and safety. By relying on visual perception research which characterize the flicker perception thresholds for different frequencies and modulation depths, we focus in this paper on analyzing the SSVEP response near the perception threshold and we show that it is possible to design quasi-imperceptible RVS which elicits a sufficiently strong SSVEP response.

Phase Detection of Visual Evoked Potentials Applied to Brain Computer Interfacing

Brain computer interfaces (BCI) that use the steady-state-visual-evoked-potential (SSVEP) as a neural source are very promising communication restoration technologies because they require shorter calibration and can yield higher information transfer rates compared to BCIs based on other neural sources. SSVEPs elicited by high frequency (higher than 30 Hz) repetitive visual stimulation are more comfortable, safer, and less prone to cause visual fatigue. However, there is a practical limitation in the high frequency range, because only a few frequencies can be used for BCI purposes. This limitation can be overcome by using repetitive visual stimuli having the same frequency but different phases. Detecting the phase of the SSVEP from the electroencephalogram is possible through a convenient signal processing approach which concatenates spatial filtering and phase synchrony analysis. This approach is illustrated with an actual BCI implementation which was tested on 15 participants and resulted on an average bit rate of 33.2 bits-per minute and an average accuracy of 92 %. These results are comparable to state-of-the-art SSVEP-BCI performance. In this approach, however, the user experiences minimal annoyance from attending to visual stimulation.

Automatic characterization of sleep need dissipation dynamics using a single EEG signal.

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.