Classification of Complex Emotions Using EEG and Virtual Environment: Proof of Concept and Therapeutic Implication

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training.

Characterization of interstimulus interaction in the multiple auditory steady-state responses at high sound levels

Multiple auditory steady-state response (MASSR) is recommended to estimate hearing thresholds in difficult-to-test individuals. The multiple stimuli that evoke MASSR may present an interstimulus interaction (ISI) that is able to distort the generation of responses. No consensus exists on the effects of the ISI in MASSR when dealing with high sound level stimuli or cases of sensorineural hearing loss. This study investigated the effects of ISI on the amplitude and detectability of auditory steady-state responses, with a focus at and above 65 dB sound pressure level (SPL). Normal hearing (NH) and sensorineural hearing impaired (SNHI) adults were tested with different stimulus types [amplitude modulation (AM) One octave chirp (OC), and a weighted OC (WOC)], stimulus levels, and modalities (single or multiple stimuli). ISI typically attenuated response amplitude of a control stimulus caused by an interference stimulus one octave above the control stimulus. At and above 80 dB SPL, attenuations of around 50% decreased the number of detectable responses near SNHI thresholds, especially for OC and WOC. AM stimuli obtained a higher detection rate than OC and WOC when presented 10 dB above the behavioral hearing threshold of SNHI participants. Using OC in MASSR when assessing elevated thresholds might diminish accuracy on threshold estimation, and extend test duration.

Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.

The addition of functional task-oriented mental practice to conventional physical therapy improves motor skills in daily functions after stroke

BACKGROUND

Mental practice (MP) is a cognitive strategy which may improve the acquisition of motor skills and functional performance of athletes and individuals with neurological injuries.

OBJECTIVE

To determine whether an individualized, specific functional task-oriented MP, when added to conventional physical therapy (PT), promoted better learning of motor skills in daily functions in individuals with chronic stroke (13 ± 6.5 months post-stroke).

METHOD

Nine individuals with stable mild and moderate upper limb impairments participated, by employing an A1-B-A2 single-case design. Phases A1 and A2 included one month of conventional PT, and phase B the addition of MP training to PT. The motor activity log (MAL-Brazil) was used to assess the amount of use (AOU) and quality of movement (QOM) of the paretic upper limb; the revised motor imagery questionnaire (MIQ-RS) to assess the abilities in kinesthetic and visual motor imagery; the Minnesota manual dexterity test to assess manual dexterity; and gait speed to assess mobility.

RESULTS

After phase A1, no significant changes were observed for any of the outcome measures. However, after phase B, significant improvements were observed for the MAL, AOU and QOM scores (p<0.0001), and MIQ-RS kinesthetic and visual scores (p=0.003; p=0.007, respectively). The significant gains in manual dexterity (p=0.002) and gait speed (p=0.019) were maintained after phase A2.

CONCLUSIONS

Specific functional task-oriented MP, when added to conventional PT, led to improvements in motor imagery abilities combined with increases in the AOU and QOM in daily functions, manual dexterity, and gait speed.

Effect of the rest interval duration between contractions on muscle fatigue

Background

We aimed to investigate the effect of rest interval, between successive contractions, on muscular fatigue.

Methods

Eighteen subjects performed elbow flexion and extension (30 repetitions) on an isokinetic dynamometer with 80º of range of motion. The flexion velocity was 120º/s, while for elbow extension we used 5 different velocities (30, 75, 120, 240, 360º/s), producing 5 different rest intervals (2.89, 1.28, 0.85, 0.57 and 0.54 s).

Results

We observed that when the rest interval was 2.89 s there was a reduction in fatigue. On the other hand, when the rest interval was 0.54 s the fatigue was increased.

Conclusions

When the resting time was lower (0.54 s) the decline of work in the flexor muscle group was higher compared with different rest interval duration.

Maximum respiratory pressure measuring system: calibration and evaluation of uncertainty

The objective of this paper is to present a methodology for the evaluation of uncertainties in the measurements results obtained during the calibration of a digital manovacuometer prototype (DM) with a load cell sensor pressure device incorporated. Calibration curves were obtained for both pressure sensors of the DM using linear regression by weighted least squares method (WLS). Two models were built to evaluate uncertainty. One takes into account the information listed in the sensor datasheet, resulting in the maximum permissible measurement error of the manovacuometer, and the other on the WLS implemented during calibration. Considering a range of ten calibration points, it was found that calibration procedure designed using WLS modeling indicates that the range of measurement uncertainty extends from 0.2 up to 0.5 kPa. This is inside the manufacter range that extends from 1.5 up 3.5 kPa, showing adequacy for use

Optimum principal components for spatial filtering of EEG to detect imaginary movement by coherence

Several techniques have been used to improve the signal-to-noise ratio to increase the detection rate of Event Related Potentials (ERPs). This work investigates the application of spatial filtering based on principal component analysis (PCA) to detect ERP due to left-hand index finger movement imagination. The EEG signals were recorded of central derivations (C4, C2, Cz, C1 and C3), positioned according to 10-10 International System. The optimal spatial filter was found by using the first principal component and the ERP detection was obtained by magnitude squared coherence technique. The best detection rate, by using original signal (without filtering), was obtained at C2 derivation, with 54.73% for significance level of 5%. For the same significance level, the detection rate of the filtered signal was drastically improved to 96.84%. Results suggest that spatial filter by using PCA might be a very useful tool in assisting the ERP detection for movement imagination for applications on brain machine interface.

Sensations and reaction times evoked by electrical sinusoidal stimulation

OBJECTIVE

To determine whether 5 Hz and 2000 Hz sinusoidal electric currents evoke different sensations and to indirectly evaluate which peripheral nerve fibers are stimulated by these different frequencies.

METHODS

One hundred and fifty subjects chose three among eight descriptors of sensations evoked by 5 Hz and 2000 Hz currents and the results were submitted to factor analysis. In 20 subjects, reaction times to 5, 250 and 2000 Hz currents were determined at 1.1 x ST and reaction times to 5 Hz currents were also determined at 2 x ST.

RESULTS

Responses were grouped in four factors: Factor 1, which loaded mainly in descriptors related to tweezers stimulation, was higher than the other factors during 2000 Hz stimulation at 1.5 x ST. Factor 2, which loaded mainly in descriptors related to needle stimulation, was higher than the other factors during 5 Hz stimulation. Factor 1 increased and Factor 2 decreased with an increase in 5 Hz intensity from 1.5 to 4x ST. Reaction times measured from the fastest responses were significantly different: 0.57 s (0.16 to 1.60), 0.34 s (0.12 to 0.71) and 0.22s (0.08 to 0.35) for 5, 250 and 2000 Hz, respectively, and 0.22s (0.11 to 0.34) for 5 Hz at 2 x ST.

CONCLUSIONS

Sinusoidal electrical stimulation of 5 Hz and 2000 Hz evoke different sensations. At juxta-threshold intensities, RT measurements suggest that 2000 Hz stimulates Abeta-fibers, 250 Hz Abeta- or A partial differential-fibers, 5 Hz Abeta-, A partial differential- or C-fibers. The fiber type, which was initially stimulated by the lower frequencies, depended on inter-individual differences.

Use of magnitude-squared coherence to identify the maximum driving response band of the somatosensory evoked potential

The present study proposes to apply magnitude-squared coherence (MSC) to the somatosensory evoked potential for identifying the maximum driving response band. EEG signals, leads [Fpz'-Cz'] and [C3'-C4'], were collected from two groups of normal volunteers, stimulated at the rate of 4.91 (G1: 26 volunteers) and 5.13 Hz (G2: 18 volunteers). About 1400 stimuli were applied to the right tibial nerve at the motor threshold level. After applying the anti-aliasing filter, the signals were digitized and then further low-pass filtered (200 Hz, 6th order Butterworth and zero-phase). Based on the rejection of the null hypothesis of response absence (MSC(f) > 0.0060 with 500 epochs and the level of significance set at a = 0.05), the beta and gamma bands, 15-66 Hz, were identified as the maximum driving response band. Taking both leads together ("logical-OR detector", with a false-alarm rate of a = 0.05, and hence a = 0.0253 for each derivation), the detection exceeded 70% for all multiples of the stimulation frequency within this range. Similar performance was achieved for MSC of both leads but at 15, 25, 35, and 40 Hz. Moreover, the response was detected in [C3'-C4'] at 35.9 Hz and in [Fpz'-Cz'] at 46.2 Hz for all members of G2. Using the "logical-OR detector" procedure, the response was detected at the 7th multiple of the stimulation frequency for the series as a whole (considering both groups). Based on these findings, the MSC technique may be used for monitoring purposes.

Objective response detection in an electroencephalogram during somatosensory stimulation

Techniques for objective response detection aim to identify the presence of evoked potentials based purely on statistical principles. They have been shown to be potentially more sensitive than the conventional approach of subjective evaluation by experienced clinicians and could be of great clinical use. Three such techniques to detect changes in an electroencephalogram (EEG) synchronous with the stimuli, namely, magnitude-squared coherence (MSC), the phase-synchrony measure (PSM) and the spectral F test (SFT) were applied to EEG signals of 12 normal subjects under conventional somatosensory pulse stimulation to the tibial nerve. The SFT, which uses only the power spectrum, showed the poorest performance, while the PSM, based only on the phase spectrum, gave results almost as good as those of the MSC, which uses both phase and power spectra. With the latter two techniques, stimulus responses were evident in the frequency range of 20-80 Hz in all subjects after 200 stimuli (5 Hz stimulus frequency), whereas for visual recognition at least 500 stimuli are usually applied. Based on these results and on simulations, the phase-based techniques appear promising for the automated detection and monitoring of somatosensory evoked potentials.