Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

doi:10.3389/fnins.2017.00548

Cited by 15 publications

(11 citation statements)

References 74 publications

(102 reference statements)

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“…Taking a closer look into the BCI decoding literature, a variety of methods for oscillatory EEG classification problems can be found, but for the regression case the choice still is extremely limited (Wu et al, 2017) even though regression methods allow tackling highly interesting problems. Examples are the estimation of continuous mental workload levels (Frey et al, 2016;Schultze-Kraft et al, 2016), decoding the depth of cognitive processing (Nicolae et al, 2017), predicting singletrial motor performance (Meinel et al, 2016) or continuous decoding of movement trajectories (Úbeda et al, 2017). A spatial filtering solution, which solves an EEG regression problem, was provided by Dähne et al (2014) with the source power comodulation algorithm (SPoC).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

et al. 2018

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We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.

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Section: Introductionmentioning

confidence: 99%

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

et al. 2018

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“…Increasing the complexity of the stimulus [35] and the activity of the cortical structures participating in its analysis [61] is associated, generally, with the increase in the amplitude of the EP components. This conclusion, however, is drawn in rather simple experimental paradigms.…”

Section: Discussionmentioning

confidence: 99%

Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form

Kiroy¹

2018

AAP

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Response time and evoked potentials were registered for visual images related to two categories fruit and tableware as well as their verbal representations. The stimuli were presented randomly. The subjects were to attribute them regardless of the form (a word or image) to one of the categories. 11 female and 10 male subjects (average age 21.9±2.9 years) participated in the tests. 6 components of the evoked potentials were singled out: Р1 (Р66), N1 (N124), Р2 (Р180), N2 (N248), Р3 (Р331) and N3 (N456). Analysis showed that both female and male subjects demonstrated reliably longer response time for words as compared to those for corresponding images. For words, evoked potentials were registered in more complex configurations and with a shorter latency period for the early components (P1, N1) and longer latency period for the late ones (P2, N2, P3, N3). The evoked potential amplitude in response to verbal stimuli was smaller than that for visual ones. Evoked potential components in response to target stimuli (both images and words) had, in general, shorter latency. The amplitude of N1, Р2 and N2 components was lower, while that of P3 and N3 was higher for target stimuli rather than a non-target. The obtained results allow us to assume that evaluation of the type of information (verbal or visual) can be performed on early stages of stimulus perception (up to 120-150 ms). Further analysis includes either more detailed description of spatial features of the visual stimuli in parietal and occipital lobes or estimation of the semantics of a word employing the frontal and temporal areas. Decision-making on formulating a response barely depends on the manner of information presentation (visual and verbal).

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“…In non-memory cognitive systems, control signals utilizing blood-oxygen-level dependent (BOLD), evoked potentials, or spectral analysis have been incorporated as biofeedback therapy for self-optimization of attentional networks ( deBettencourt et al, 2015 ; Jiang et al, 2017 ) and as communicative tools for patients with altered levels of consciousness ( Luauté et al, 2015 ; Ortner et al, 2017 ). These approaches have also been used to assess and predict the depth of cognitive processing, reflected by the level of task difficulty, in memory, language, and visual task domains ( Nicolae et al, 2017 ).…”

Section: Current Advancesmentioning

confidence: 99%

“…Investigators have used changes in regional LFP oscillatory activity before ( Guderian et al, 2009 ; Fell et al, 2011 ; Hanslmayr and Staudigl, 2014 ; Merkow et al, 2014 ) and during encoding ( Klimesch et al, 1997 ; Fell et al, 2001 ; Sederberg et al, 2007 ), as well as temporally precise single unit hippocampal activity ( Rutishauser et al, 2010 ), to predict subsequent recall. Utilizing multimodal analysis of event-related potentials and event-related desynchronization, a recent study was able to successfully predict depth of cognitive processing in memory, language, and visual imagination task domains ( Nicolae et al, 2017 ). In other cognitive realms such as experiential learning, executive control, and dynamic online cognitive performance, less is known about local neural signatures.…”

Section: Introductionmentioning

confidence: 99%

“…The features of the functional network can be quantified using numerous statistics commonly referred to as graph statistics, network measures, or connectomic metrics. The quantification of network architecture in patterns of FC can provide robust estimates of task-dependent interregional coordination, and its relation to multisensory processing, cognition, memory, and learning ( Friston, 1997 ; Tononi, 1998 ; Sporns et al, 2004 ; Miller et al, 2007 ; Canolty et al, 2009 ; Bassett et al, 2015 ; McGregor and Gribble, 2017 ; Nicolae et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

et al. 2018

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Brain computer interfaces (BCIs) have been applied to sensorimotor systems for many years. However, BCI technology has broad potential beyond sensorimotor systems. The emerging field of cognitive prosthetics, for example, promises to improve learning and memory for patients with cognitive impairment. Unfortunately, our understanding of the neural mechanisms underlying these cognitive processes remains limited in part due to the extensive individual variability in neural coding and circuit function. As a consequence, the development of methods to ascertain optimal control signals for cognitive decoding and restoration remains an active area of inquiry. To advance the field, robust tools are required to quantify time-varying and task-dependent brain states predictive of cognitive performance. Here, we suggest that network science is a natural language in which to formulate and apply such tools. In support of our argument, we offer a simple demonstration of the feasibility of a network approach to BCI control signals, which we refer to as network BCI (nBCI). Finally, in a single subject example, we show that nBCI can reliably predict online cognitive performance and is superior to certain common spectral approaches currently used in BCIs. Our review of the literature and preliminary findings support the notion that nBCI could provide a powerful approach for future applications in cognitive prosthetics.

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Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

Cited by 15 publications

References 74 publications

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems

Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form

Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

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