Linear Spatial Integration for Single-Trial Detection in Encephalography

Parra, Lucas C.; Alvino, Christopher; Tang, Akaysha C.; Pearlmutter, Barak A.; Yeung, Nick; Osman, Allen; Sajda, Paul

doi:10.1006/nimg.2002.1212

Cited by 168 publications

(143 citation statements)

References 21 publications

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“…We compared the performance of the single-trial topographic analysis with two widely used single-trial classification techniques: logistic regression (Parra et al, 2002(Parra et al, , 2005Ratcliff et al, 2009) and SVM (Rieger et al, 2008;Taghizadeh-Sarabi et al, 2014). Despite similar levels of decoding performance across all subjects, the single-trial topographic analysis gave significant results at the single-subject level for more subjects than either of the two techniques.…”

Section: Comparison With Other Single-trial Methodsmentioning

confidence: 99%

“…The first one is logistic regression (Parra et al, 2002(Parra et al, , 2005, an algorithm which has been widely applied for studying perceptual decision-making (Philiastides and Sajda, 2006;Ratcliff et al, 2009) and in single-trial decoding in general (Brandmeyer et al, 2013;Farquhar and Hill, 2013). The second is Support Vector Machines (SVM), a powerful machine learning technique which has been used in various decoding applications (SVM; (Rieger et al, 2008;Salvaris and Sepulveda, 2009;Schulz et al, 2012;Taghizadeh-Sarabi et al, 2014)).…”

Section: Comparison With Other Single-trial Techniquesmentioning

confidence: 99%

“…All the computations have been performed based on the matlab scripts provided by the Laboratory for Intelligent Imaging and Neural Computing (http://liinc.bme.columbia .edu/mainTemplate.htm?liinc downloads.htm) and described in (Parra et al, 2002(Parra et al, , 2005.…”

Section: Comparison With Other Single-trial Techniquesmentioning

confidence: 99%

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Quantifying the time for accurate EEG decoding of single value-based decisions

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

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h i g h l i g h t s• We outline a novel method for assessing time-unlocked topographic EEG activity.• We apply this method for accurately predicting accept vs. reject decisions.• The time when decisions have been made is modulated by task difficulty.• It occurs well before subjects' button press (∼340 ms).• This time is compatible with a diffusion process, evaluated at behavioral level. a r t i c l e i n f o b s t r a c tBackground: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. New method: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. Results: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-bytrial basis. Typical latencies of when decisions are made were detected at ∼500 ms for 'Easy' vs. ∼700 ms for 'Hard' decisions, well before subjects' response (∼340 ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. Comparison with existing method(s):We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. Conclusions: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

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Section: Comparison With Other Single-trial Methodsmentioning

confidence: 99%

Section: Comparison With Other Single-trial Techniquesmentioning

confidence: 99%

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Quantifying the time for accurate EEG decoding of single value-based decisions

Tzovara

Chavarriaga

Lucia

2015

Journal of Neuroscience Methods

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“…To identify decision-related activity in the EEG signal, we used linear discrimination analysis as developed by Philiastides and colleagues (Parra et al, 2002;Philiastides and Sajda, 2006;Philiastides et al, 2006). Linear discriminant analysis performs logistic regression of binary data on multivariate EEG data to identify spatial weighting vectors (w) across electrodes that maximally discriminate between conditions of interest (e.g., a face or car stimulus).…”

Section: Experimental Designmentioning

confidence: 99%

From Image Statistics to Scene Gist: Evoked Neural Activity Reveals Transition from Low-Level Natural Image Structure to Scene Category

et al. 2013

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The visual system processes natural scenes in a split second. Part of this process is the extraction of "gist," a global first impression. It is unclear, however, how the human visual system computes this information. Here, we show that, when human observers categorize global information in real-world scenes, the brain exhibits strong sensitivity to low-level summary statistics. Subjects rated a specific instance of a global scene property, naturalness, for a large set of natural scenes while EEG was recorded. For each individual scene, we derived two physiologically plausible summary statistics by spatially pooling local contrast filter outputs: contrast energy (CE), indexing contrast strength, and spatial coherence (SC), indexing scene fragmentation. We show that behavioral performance is directly related to these statistics, with naturalness rating being influenced in particular by SC. At the neural level, both statistics parametrically modulated single-trial event-related potential amplitudes during an early, transient window (100 -150 ms), but SC continued to influence activity levels later in time (up to 250 ms). In addition, the magnitude of neural activity that discriminated between man-made versus natural ratings of individual trials was related to SC, but not CE. These results suggest that global scene information may be computed by spatial pooling of responses from early visual areas (e.g., LGN or V1). The increased sensitivity over time to SC in particular, which reflects scene fragmentation, suggests that this statistic is actively exploited to estimate scene naturalness.

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“…It can occur when one hand is prepared and held in readiness for a delayed response (e.g., Leuthold, Sommer, & Ulrich, 1996;Osman et al, 1995; or when a hand response is prepared but then inhibited before its overt execution (e.g., De Jong et al, 1990;Miller & Hackley, 1992;Osman et al, 1992). Lateralized motor potentials can also occur during imagined movements of the hand (Beisteiner, Hollinger, Lindinger, Lang, & Berthoz, 1995;Osman, Müller, Syre, & Russ, 2005;Parra et al, 2002), as will be demonstrated here.The good temporal resolution of ERPs provides a number of avenues by which that portion of brain activity closely linked to a particular cognitive or behavioral event can be isolated from Regan, 1989). These ERPs are typically represented in the frequency domain, wherein power is plotted as a function of frequency.…”

mentioning

confidence: 65%

The beat goes on: Rhythmic modulation of cortical potentials by imagined tapping.

Osman¹,

Albert²,

Ridderinkhof³

et al. 2006

Journal of Experimental Psychology: Human Perception and Perfor

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A frequency analysis was used to tag cortical activity from imagined rhythmic movements. Participants synchronized overt and imagined taps with brief visual stimuli presented at a constant rate, alternating between left and right index fingers. Brain potentials were recorded from across the scalp and topographic maps made of their power at the alternation frequency between left and right taps. Two prominent power foci occurred in each hemisphere for both overt and imagined taps, one over sensorimotor cortex and the other over posterior parietal cortex, with homologous foci in opposite hemispheres arising from oscillations 180° out of phase. These findings demonstrate temporal isomorphism at a neural level between overt and imagined movements and illustrate a new approach to studying covert actions. Keywordscovert motor processes; motor imagery; synchronization tapping; movement-related brain potentials; frequency taggingThe present study is concerned with a type of motor-mind reading. We propose a new method for studying covert motor processes. The method employs movement-related brain potentials to monitor cortical motor areas in the absence of overt movement. Participation of these areas in specific covert acts is revealed by the temporal and spatial pattern of potentials. To illustrate the method, it is applied here to a type of covert motor process known as motor imagery or mental simulation. As will be seen, people can drive their cortical motor areas with a pattern quite similar to that during overt movement while producing little or no muscle activity. Moreover, the temporal relations observed between responses from different neural populations suggest that similar motor programs were run during overt and imagined actions.Correspondence concerning this article should be addressed to Allen Osman, Department of Psychology, University of Pennsylvania, 3401 Walnut Street, Suite 302C, Philadelphia, PA 19104. Email: aosman@psych.upenn NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptTo better explain why one would want to engage in motor-mind reading, we will provide some background on covert motor processes in general and motor imagery in particular. Details of the method then will be presented. Covert Motor ProcessesCovert motor processes are motor processes that occur with little or no overt movement or muscle activity. These cognitive processes are motoric in the sense that they are implemented by the same neural machinery involved in overt movement, and perform computations similar to those necessary to produce or control overt movement. Such processes are quite pervasive. For example, motor processes occur without immediate overt consequences when a movement is prepared and then held in readiness until the proper moment (e.g., Leuthold, Sommer, & Ulrich, 1996;Osman, Moore, & Ulrich, 1995 or inhibited before becoming overt (e.g., De Jong, Coles, Logan, & Gratton, 1990;Miller & Hackley, 1992;Osman, Bashore, Coles, Donchin, & Meyer, 1992). Sometimes motor processes are cover...

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Linear Spatial Integration for Single-Trial Detection in Encephalography

Cited by 168 publications

References 21 publications

Quantifying the time for accurate EEG decoding of single value-based decisions

Quantifying the time for accurate EEG decoding of single value-based decisions

From Image Statistics to Scene Gist: Evoked Neural Activity Reveals Transition from Low-Level Natural Image Structure to Scene Category

The beat goes on: Rhythmic modulation of cortical potentials by imagined tapping.

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