GCHSR 2016 Junior Journal Club

Slides:



Advertisements
Similar presentations
Dynamic causal Modelling for evoked responses Stefan Kiebel Wellcome Trust Centre for Neuroimaging UCL.
Advertisements

Dynamic Causal Modelling for ERP/ERFs
Neuroimaging for Cognitive Research
All slides © S. J. Luck, except as indicated in the notes sections of individual slides Slides may be used for nonprofit educational purposes if this copyright.
Electroencephalogram (EEG) and Event Related Potentials (ERP) Lucy J. Troup 28 th January 2008 CSU Symposium on Imaging.
Dynamic Causal Modelling for ERP/ERFs Valentina Doria Georg Kaegi Methods for Dummies 19/03/2008.
Early auditory novelty processing in humans: auditory brainstem and middle-latency responses Slabu L, Grimm S, Costa-Faidella J, Escera C.
What do you need to know about DCM for ERPs/ERFs to be able to use it?
NEUR 3680 Midterm II Review Megan Metzler
Read this article for next week: A Neural Basis for Visual Search in Inferior Temporal Cortex Leonardo Chelazzi et al. (1993) Nature.
Induced Brain Waves by Binaural Beats: A Study on Numerosity.
MEG Experiments Stimulation and Recording Setup Educational Seminar Institute for Biomagnetism and Biosignalanalysis February 8th, 2005.
Read this article for Wednesday: A Neural Basis for Visual Search in Inferior Temporal Cortex Leonardo Chelazzi et al. (1993) Nature.
Test Oct. 21 Review Session Oct 19 2pm in TH201 (that’s here)
Read this article for Friday next week [1]Chelazzi L, Miller EK, Duncan J, Desimone R. A neural basis for visual search in inferior temporal cortex. Nature.
Early Selection Early Selection model postulated that attention acted as a strict gate at the lowest levels of sensory processing Based on concept of a.
Attention as Information Selection. Early Selection Early Selection model postulated that attention acted as a strict gate at the lowest levels of sensory.
Theoretical Models of Attention. Broadbent (1958) conceptualized attention as information processing Used a cuing paradigm to show that attentional selection.
Theoretical Models of Attention. Broadbent (1958) conceptualized attention as information processing Used a cuing paradigm to show that attentional selection.
Audiovisual Multisensory Facilitation: A Fresh Look at Neural Coactivation and Inverse Effectiveness. Lynnette Leone North Dakota State University.
Rapid Serial Visual Presentation (RSVP) Task (abbreviated sequence) Simulates saccadic vision Used to gauge speed of visual object recognition Thorpe et.
Consequences of Attentional Selection Single unit recordings.
The ‘when’ pathway of the right parietal lobe L. Battelli A. Pascual - LeoneP. Cavanagh.
Change blindness and time to consciousness Professor: Liu Student: Ruby.
1. 2 Abstract - Two experimental paradigms : - EEG-based system that is able to detect high mental workload in drivers operating under real traffic condition.
Participants: 57 children (6-8 years old, 35 boys) participated in experiments. All were schoolchildren in first class of elementary school in Novosibirsk,
DCM for ERPs/EFPs Clare Palmer & Elina Jacobs Expert: Dimitris Pinotsis.
Localization of Auditory Stimulus in the Presence of an Auditory Cue By Albert Ler.
Automatic online control of motor adjustments -P NANDHA KUMAR.
Zatorre et al. Nature Neuroscience, 2002 These animations illustrate the stimulus sequences used in Experiments 1 and 2. For each experiment, the animation.
Neurophysiologic correlates of cross-language phonetic perception LING 7912 Professor Nina Kazanina.
Neural Basis of the Ventriloquist Illusion Bonath, Noesselt, Martinez, Mishra, Schwiecker, Heinze, and Hillyard.
Dynamic Causal Modelling for EEG and MEG
Introduction Can you read the following paragraph? Can we derive meaning from words even if they are distorted by intermixing words with numbers? Perea,
Näätänen et al. (1997) Language-specific phoneme representations revealed by electric and magnetic brain responses. Presented by Viktor Kharlamov September.
Temporary suppression of visual processing in an RSVP task: an attention blink? By Raymond, Shapiro, & Arnell JEP:HPP.
Dynamic Causal Model for evoked responses in MEG/EEG Rosalyn Moran.
ANT Z=52 R ACUE - PASSIVE VCUE - PASSIVE 1300 msVoltageCSD.31uV.03uV/cm 2 AIM We investigate the mechanisms of this hypothesized switch-ERP.
ERPs in language acquisition
“PASSIVE” AND “ACTIVE” P300 – TWO SYSTEMS OF GENERATION (P300 IN PATIENTS WITH FOCAL BRAIN DAMAGES) L.Oknina, E. Sharova, O.Zaitsev, E.Masherow INSTITUTE.
Robert W. McCarley, Presenter Cindy Wible, Marek Kubicki ( generated fMRI data), and Dean Salisbury (generated ERP data) Harvard, VA Boston Healthcare.
Multichannel auditory processing in determining gaze direction ACG Progress Meeting 31 January 2008.
Stimulation Effects in SSVEP-based BCIs Jordi Bieger, July 8, 2010.
Examples of Experimental Design
Introduction to ERPs.
Precedence-based speech segregation in a virtual auditory environment
What made you respond face (or word)? Something in your brain made you decide face or word. Can we determine where this decision.
[Ran Manor and Amir B.Geva] Yehu Sapir Outlines Review
S. Kramer1, K. Tucker1, A.L. Moro1, E. Service1, J.F. Connolly1
Neurofeedback of beta frequencies:
Copyright © American Speech-Language-Hearing Association
Copyright © American Speech-Language-Hearing Association
A proposed Proof-of-Concept experiment
Figure 1 General framework of brain–computer interface (BCI) systems
Lecture 22. Saccades 2 Reading Assignments: Reprint
Word Imagery Effects on Explicit and Implicit Memory
Dynamic Causal Model for evoked responses in M/EEG Rosalyn Moran.
Dynamic Causal Modelling for ERP/ERFs
An Exploration of BCI2000 Utility Across Multiple Sessions
EE513 Audio Signals and Systems
Auditory Scene Analysis
Dynamic Causal Modelling for M/EEG
Ciaran Cooney, Raffaella Folli, Damien Coyle  iScience 
Posterior parietal cortex
Machine Learning for Visual Scene Classification with EEG Data
Common ERPs BCS204 Week 5.2 2/13/2019.
Dynamic Causal Modelling for evoked responses
Christian Büchel, Jond Morris, Raymond J Dolan, Karl J Friston  Neuron 
Vahe Poghosyan, Andreas A. Ioannides  Neuron 
Localization behavior in ITD-alone condition and head turn analysis.
Presentation transcript:

GCHSR 2016 Junior Journal Club May 18, 2016 Erol Ozmeral

Brain-computer interfaces (BCI) A BCI is a setup in which a device is directly controlled by neural pathways. Can be used to communicate, manipulate objects, and steer computational processing. Typical BCI’s are: Visual paradigms Binary classifiers Aimed to treat ALS and CLIS patients

Previous Auditory BCI Hill et al (2005) Kanoh et al (2008) Measured ERPs Binary BCI Two sequences in each ear (different ISIs in each stream) Kanoh et al (2008) ERPs to two oddball streams in right ear only Purported the ability to extend to more than two streams

P300 Described as the positive deflection in the EEG signal roughly 300 ms after the onset of an event. Typically in response to rare events among more common events (i.e., oddball) P300 has been shown to be stronger for overt fixation both in auditory and visual domain.

Spatial Hearing and Attention Our ability to localize auditory objects in space are subject to selective attention (Mondor and Zatorre 1995; Teder-Salejarvi and Hillyard 1998). Spatial separation has been shown to be a reliable parameter for differentiating standard (common) and deviant (rare) sounds (Sonnadara et al 2006; Deouell et al 2006). These studies used the mismatched-negativity measure, not P300. The present experiment looked at a P300 evoked response to an spatially deviant auditory stimulus.

Experimental setup “A multichannel, low-latency firewire soundcard from M-Audio (M-Audio Firewire 410) was used to individually control the low-budget, offthe-shelf computer speakers.”

Electrophysiology (7 subjects) Presentation level: ~58 dB Stim: 75 ms BPN (0.15-8kHz) Conditions C1000: random presentation from 1 of 8 speakers, 1 s ISI, subjects were asked to focus on a target direction.

BCI setup (5 subjects) Only 5 frontal speakers Stimuli were now discriminated by pitch and spatial location 3 Conditions C300: ISI = 300 ms C175: ISI = 175 ms C300s: Pitch property only

The guts of the system

Electrophys Results

BCI Results

Continued…

Discussion Current setup was offline, not real-time Subject were “healthy”, and not many were used Environment was barely controlled for Nevertheless, most data was reliably (>70%) used to make target selections after very few iterations.