4,236 research outputs found
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
Fully automated decoding of human activities and intentions from direct
neural recordings is a tantalizing challenge in brain-computer interfacing.
Most ongoing efforts have focused on training decoders on specific, stereotyped
tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in
natural settings requires adaptive strategies and scalable algorithms that
require minimal supervision. Here we propose an unsupervised approach to
decoding neural states from human brain recordings acquired in a naturalistic
context. We demonstrate our approach on continuous long-term
electrocorticographic (ECoG) data recorded over many days from the brain
surface of subjects in a hospital room, with simultaneous audio and video
recordings. We first discovered clusters in high-dimensional ECoG recordings
and then annotated coherent clusters using speech and movement labels extracted
automatically from audio and video recordings. To our knowledge, this
represents the first time techniques from computer vision and speech processing
have been used for natural ECoG decoding. Our results show that our
unsupervised approach can discover distinct behaviors from ECoG data, including
moving, speaking and resting. We verify the accuracy of our approach by
comparing to manual annotations. Projecting the discovered cluster centers back
onto the brain, this technique opens the door to automated functional brain
mapping in natural settings
ECoG high gamma activity reveals distinct cortical representations of lyrics passages, harmonic and timbre-related changes in a rock song
Listening to music moves our minds and moods, stirring interest in its neural underpinnings. A multitude of compositional features drives the appeal of natural music. How such original music, where a composer's opus is not manipulated for experimental purposes, engages a listener's brain has not been studied until recently. Here, we report an in-depth analysis of two electrocorticographic (ECoG) data sets obtained over the left hemisphere in ten patients during presentation of either a rock song or a read-out narrative. First, the time courses of five acoustic features (intensity, presence/absence of vocals with lyrics, spectral centroid, harmonic change, and pulse clarity) were extracted from the audio tracks and found to be correlated with each other to varying degrees. In a second step, we uncovered the specific impact of each musical feature on ECoG high-gamma power (70–170 Hz) by calculating partial correlations to remove the influence of the other four features. In the music condition, the onset and offset of vocal lyrics in ongoing instrumental music was consistently identified within the group as the dominant driver for ECoG high-gamma power changes over temporal auditory areas, while concurrently subject-individual activation spots were identified for sound intensity, timbral, and harmonic features. The distinct cortical activations to vocal speech-related content embedded in instrumental music directly demonstrate that song integrated in instrumental music represents a distinct dimension in complex music. In contrast, in the speech condition, the full sound envelope was reflected in the high gamma response rather than the onset or offset of the vocal lyrics. This demonstrates how the contributions of stimulus features that modulate the brain response differ across the two examples of a full-length natural stimulus, which suggests a context-dependent feature selection in the processing of complex auditory stimuli
Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools
While detecting and interpreting temporal patterns of non–verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human–centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement
MAD-EEG: an EEG dataset for decoding auditory attention to a target instrument in polyphonic music
International audienceWe present MAD-EEG, a new, freely available dataset for studying EEG-based auditory attention decoding considering the challenging case of subjects attending to a target instrument in polyphonic music. The dataset represents the first music-related EEG dataset of its kind, enabling, in particular, studies on single-trial EEG-based attention decoding, while also opening the path for research on other EEG-based music analysis tasks. MAD-EEG has so far collected 20-channel EEG signals recorded from 8 subjects listening to solo, duo and trio music excerpts and attending to one pre-specified instrument. The proposed experimental setting differs from the ones previously considered as the stimuli are polyphonic and are played to the subject using speakers instead of headphones. The stimuli were designed considering variations in terms of number and type of instruments in the mixture, spatial rendering, music genre and melody that is played. Preliminary results obtained with a state-of-the-art stimulus reconstruction algorithm commonly used for speech stimuli show that the audio representation reconstructed from the EEG response is more correlated with that of the attended source than with the one of the unattended source, proving the dataset to be suitable for such kind of studies
Detecting and interpreting conscious experiences in behaviorally non-responsive patients
Decoding the contents of consciousness from brain activity is one of the most challenging frontiers of cognitive neuroscience. The ability to interpret mental content without recourse to behavior is most relevant for understanding patients who may be demonstrably conscious, but entirely unable to speak or move willfully in any way, precluding any systematic investigation of their conscious experience. The lack of consistent behavioral responsivity engenders unique challenges to decoding any conscious experiences these patients may have solely based on their brain activity. For this reason, paradigms that have been successful in healthy individuals cannot serve to interpret conscious mental states in this patient group. Until recently, patient studies have used structured instructions to elicit willful modulation of brain activity according to command, in order to decode the presence of willful brain-based responses in this patient group. In recent work, we have used naturalistic paradigms, such as watching a movie or listening to an audio-story, to demonstrate that a common neural code supports conscious experiences in different individuals. Moreover, we have demonstrated that this code can be used to interpret the conscious experiences of a patient who had remained non-responsive for several years. This approach is easy to administer, brief, and does not require compliance with task instructions. Rather, it engages attention naturally through meaningful stimuli that are similar to the real-world sensory information in a patient\u27s environment. Therefore, it may be particularly suited to probing consciousness and revealing residual brain function in highly impaired, acute, patients in a comatose state, thus helping to improve diagnostication and prognostication for this vulnerable patient group from the critical early stages of severe brain-injury
Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification
Objective. Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form concurrent information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for use in a brain–computer interface. Approach. In a multi-streamed oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips. Each instrument formed an oddball stream with its own specific standard stimuli (a repetitive musical pattern) and oddballs (deviating musical pattern). Main results. Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants. Significance. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain–computer interface and music research
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Representational dynamics across multiple timescales in human cortical networks
Human cognition occurs at multiple timescales, including immediate processing of the ongoing experiences and slowly drifting higher-level thoughts. To understand how the brain selects and represents these various types of information to guide behavior, this thesis examined representational content within sensory regions, multiple demand (MD) network, and default mode network (DMN). Chapter 1 provides a background review of the current literature. It begins by reviewing experimental investigations of component visual processes that unfold over time. Next, the MD network is introduced as a collection of frontal and parietal regions involved in implementing cognitive control by assembling the required operations for task-relevant behavior. Finally, the DMN is introduced in the context of temporal processing hierarchies, with focus on its representation of situation models summarizing interactions among entities and the environment. The first experiment, presented in Chapter 2, used EEG/MEG to track multiple component processes of selective attention. Five distinct processing operations with different time-courses were quantified, including representation of visual display properties, target location, target identity, behavioral significance, and finally, possible reactivation of the attentional template. Chapter 3 used fMRI to examine neural representations of task episodes, which are temporally organized sequences of steps that occur within a given context. It was found that MD and visual regions showed sensitivity to the fine structure of the contents within a task. DMN regions showed gradual change throughout the entire task, with increased activation at the offset of the entire episode. Chapter 4 analyzed activation profiles of DMN regions using six diverse tasks to examine their functional convergence during social, episodic, and self-referential thought. Results supported proposals of separate subsystems, yet also suggest integration within the DMN. The final chapter, Chapter 5, provides an extended discussion of theoretical concepts related to the three experiments and proposes possible avenues for further research
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The role of the default mode network in contextual control
While extensive theories outline the importance of meaningful context in guiding goal directed behaviour, little evidence has emerged about the underlying cognitive mechanisms involved. This thesis aims to addresses this gap in the literature by integrating two commonly disparate topics in neuroscience: cognitive control and the default mode network.
Chapter 2 considers why current studies of contextual control do not implicate DMN regions by comparing context-dependent decision making using rich, meaningful scenes, in comparison to arbitrary letter stimuli. DMN regions of the posterior cingulate cortex, parahippocampus and posterior inferior parietal cortex are found to show increased activity during decision making in the lifelike context only.
Chapter 3 asks whether regions beyond the ‘task-positive’ multiple demand network are necessary for adequate performance in more lifelike naturalistic tasks. This neuropsychology experiment used behavioural data accumulated from brain lesioned patients across a series of naturalistic tasks and a standard IQ task. Naturalistic tasks were found to capture control processes beyond IQ and multiple demand network function, most likely depending on many processes and brain regions.
Chapter 4 aims to understand to what extent the DMN contributes to non-spatial executive tasks. Replicating (Crittenden et al. 2015), DMN regions were found to represent the broader task domain and respond with greater activation to larger task switches and task restarts. A role for the DMN in transitions between distinct cognitive tasks is suggested.
Chapter 5 assesses an alternative explanation for the switch effects of the previous chapter. The fMRI experiment presented in this chapter asks whether the activation of the DMN at cognitive transitions reflects changes in task rule retrieval difficulty instead of degree of task switch. To this end, this study directly manipulated the rule retrieval demands. Contrary to the retrieval account, increased retrieval demand led to reduced DMN activity, accompanied by increased activation in MD regions.This PhD was funded by the Medical Research Counci
When the Brain Plays a Game: Neural Responses to Visual Dynamics during Naturalistic Visual Tasks
Many day-to-day tasks involve processing of complex visual information in a continuous stream. While much of our knowledge on visual processing has been established from reductionist approaches in lab-controlled settings, very little is known about the processing of complex dynamic stimuli experienced in everyday scenarios. Traditional investigations employ event-related paradigms that involve presentation of simple stimuli at select locations in visual space and discrete moments in time. In contrast, visual stimuli in real-life are highly dynamic, spatially-heterogeneous, and semantically rich. Moreover, traditional experiments impose unnatural task constraints (e.g., inhibited saccades), thus, it is unclear whether theories developed under the reductionist approach apply in naturalistic settings. Given these limitations, alternative experimental paradigms and analysis methods are necessary. Here, we introduce a new approach for investigating visual processing, applying the system identification (SI) framework. We investigate the modulation of stimulus-evoked responses during a naturalistic task (i.e., kart race game) using non-invasive scalp recordings.
In recent years, multivariate modeling approaches have become increasingly popular for assessing neural response to naturalistic stimuli. Encoding models use stimulus patterns to predict brain responses and decoding models use patterns of brain responses to predict stimulus that drove these responses. In this dissertation, we employ a hybrid method that “encodes” the stimulus to predict “decoded” brain responses. Using this approach, we measure the stimulus-response correlation (SRC), i.e. temporal correlation of neural response and dynamic stimulus. This SRC can be used to assess the strength of stimulus-evoked activity to uniquely experienced naturalistic stimulus. To demonstrate this, we measured the SRC during a kart race videogame. We find that SRC increased with active play of the game, suggesting that stimulus-evoked activity is modulated by the visual task demands. Furthermore, we analyzed the selectivity of neural response across the visual space. While it is well-established that neural response is spatially selective to discrete stimulus, it is unclear whether this is true during naturalistic stimulus presentation. To assess this, we measured the correlation of neural response with optical flow magnitude at individual locations on the screen during the videogame. We find that the SRC is greater for locations in space that are task-relevant, enhancing during active play. Moreover, the spatial selectivity differs across scalp locations, which suggest that individual brain regions are spatially selective to different visual dynamics.
In summary, we leverage the SI framework to investigate visual processing during a naturalistic stimulus presentation, extending visual research to ecologically valid paradigms. Moreover, we demonstrate spatial selectivity of neural response that are task-relevant. Overall, our findings shed new insights about the stimulus-evoked neural response to visual dynamics during a uniquely experienced naturalistic visual task. Taken together, this dissertation work makes a significant contribution towards understanding how visual dynamics and task behavior affects neural responses in naturalistic conditions
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