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PhD Student, Indian Institute of Technology Kanpur


Holistic detection of ERP components, N1, N2, P1,P2,P3 and P4 for Brain computer Interface system.

My research work is to study Event Related Potentials (ERPs) with possible implications in design of brain computer Interface system. ERPs are a specific type of evoked brain potentials that are induced using an external stimulus which can be either visual, aural or haptic or even a combination of these three. Brain computer interface looks at the possibilities of having a direct communication between brain and external environment without using the peripheral nerves linking to muscles; the input from brain can be in the form of Electroencephalographic signals (EEG), Magnetoencephalographs (MEG), functional MRI, etc. Presented work is based on brain signals acquired using EEG. The EEG is representative of the brain process that happen between the instance of administering the stimulus and the resultant behavioural response for the stimulus. The ERP are potentials that are specific to the applied stimulus. For example, the time and nature of an ERP for a green colored dot as a visual stimulus will be different from the ERP of a 250 Hz aural tone. Studying these ERPs will give us some insights to the functioning of the brain. I have used visual stimulus based on an oddball paradigm to elicit ERPs; Here, the task is to differentiate a rare target from frequent non-target. The stimulus appears as continuous flashes involving a target (low probability) and a non-target (high probability); the user counts number of times a target was flashed. Attention to target and non-target stimulus produce distinct patterns that can be seen in an electroencephalograph (EEG), the pattern corresponding to target usually produces an ERP. The ERP waveform has negative and positive peaks, known as N1, N2, P1, P2, P3 and P4. The P3 component of ERP is often used in brain computer interface system as an input from brain, which is converted to a command using signal processing techniques like feature extraction and pattern recognition, for communication. The P3 component has a limitation of diminishing as the user gets habituated to the stimulus. In order to mitigate this limitation, a holistic approach is being built wherein features based on a compound of ERP components are identified to be used as trigger for command generation. This approach is expected to develop into a more mature model for a brain computer interface system.


Is P3 a strategic or a tactical component? Relationships of P3 sub-components to response times in oddball tasks with go, no-go and choice responses.

Abstract: P3 (viz. P300) is a most prominent component of event-related EEG potentials recorded during task performance. There has been long-standing debate about whether the process reflected by P3 is tactical or strategic, i.e., required for making the present response or constituting some overarching process. Here, we used residue iteration decomposition (RIDE) to delineate P3 subcomponents time-locked to responses and tested for the temporal relations between P3 components and response times (RTs). Data were obtained in oddball tasks (i.e., tasks presenting two stimuli, one rarely and one frequently) with rare and frequent go, no-go, or choice responses (CRs). As usual, rare-go P3s were large at Pz and rare no-go P3s at FCz. Notably, P3s evoked with rare CRs were large at either site, probably comprising both go and no-go P3. Throughout, with frequent and rare responses, P3 latencies coincided with RTs. RIDE decomposed P3 complexes into a large CPz-focused C-P3 and an earlier Pz-focused response-locked R-P3. R-P3 had an additional large fronto-central focus with rare CRs, modeling the no-go-P3 part, suggesting that the process reflected by no-go P3 is tightly time-locked to making the alternative response. R-P3 coincided with the fast RTs to frequent stimuli and C-P3 coincided with the slower RTs to rare stimuli. Thus, the process reflected by C-P3 might be required for responding to rare events, but not to frequent ones. We argue that these results are nevertheless compatible with a tactical role of P3 because responses may not be contingent on stimulus analysis with frequent stimuli.

Pub.: 30 Aug '16, Pinned: 31 Jul '17

Brain fingerprinting: a comprehensive tutorial review of detection of concealed information with event-related brain potentials.

Abstract: Brain fingerprinting (BF) detects concealed information stored in the brain by measuring brainwaves. A specific EEG event-related potential, a P300-MERMER, is elicited by stimuli that are significant in the present context. BF detects P300-MERMER responses to words/pictures relevant to a crime scene, terrorist training, bomb-making knowledge, etc. BF detects information by measuring cognitive information processing. BF does not detect lies, stress, or emotion. BF computes a determination of "information present" or "information absent" and a statistical confidence for each individual determination. Laboratory and field tests at the FBI, CIA, US Navy and elsewhere have resulted in 0% errors: no false positives and no false negatives. 100% of determinations made were correct. 3% of results have been "indeterminate." BF has been applied in criminal cases and ruled admissible in court. Scientific standards for BF tests are discussed. Meeting the BF scientific standards is necessary for accuracy and validity. Alternative techniques that failed to meet the BF scientific standards produced low accuracy and susceptibility to countermeasures. BF is highly resistant to countermeasures. No one has beaten a BF test with countermeasures, despite a $100,000 reward for doing so. Principles of applying BF in the laboratory and the field are discussed.

Pub.: 02 Apr '13, Pinned: 31 Jul '17

Brain fingerprinting field studies comparing P300-MERMER and P300 brainwave responses in the detection of concealed information.

Abstract: Brain fingerprinting detects concealed information stored in the brain by measuring brainwave responses. We compared P300 and P300-MERMER event-related brain potentials for error rate/accuracy and statistical confidence in four field/real-life studies. 76 tests detected presence or absence of information regarding (1) real-life events including felony crimes; (2) real crimes with substantial consequences (either a judicial outcome, i.e., evidence admitted in court, or a $100,000 reward for beating the test); (3) knowledge unique to FBI agents; and (4) knowledge unique to explosives (EOD/IED) experts. With both P300 and P300-MERMER, error rate was 0 %: determinations were 100 % accurate, no false negatives or false positives; also no indeterminates. Countermeasures had no effect. Median statistical confidence for determinations was 99.9 % with P300-MERMER and 99.6 % with P300. Brain fingerprinting methods and scientific standards for laboratory and field applications are discussed. Major differences in methods that produce different results are identified. Markedly different methods in other studies have produced over 10 times higher error rates and markedly lower statistical confidences than those of these, our previous studies, and independent replications. Data support the hypothesis that accuracy, reliability, and validity depend on following the brain fingerprinting scientific standards outlined herein.

Pub.: 23 Jul '13, Pinned: 31 Jul '17

The component structure of event-related potentials in the p300 speller paradigm.

Abstract: We investigated the componential structure of event-related potentials elicited while participants use the P300 BCI. Six healthy participants "typed" all characters in a 6 × 6 matrix twice in a random sequence. A principal component analysis indicated that in addition to the P300, target flashes elicited an earlier frontal positivity, possibly a Novelty P3. The amplitudes of both P300 and the Novelty P3 varied with the matrix row in which the target character was located. However, the P300 elicited by row flashes was largest for targets in the lower part of the matrix, whereas the Novelty P3 elicited by column flashes was largest in the top part. Classification accuracy using stepwise linear discriminant analysis mirrored the pattern in the Novelty P3 (an accuracy difference of 0.1 between rows 1 and 6). When separate classifiers were generated to rely solely on the P300 or solely on the Novelty P3, the latter function led to higher accuracy (a mean accuracy difference of about 0.2 between classifiers). A possible explanation is that some nontarget flashes elicit a P300, leading to lower selection accuracy of the respective classifier. In an additional set of data from six different participants we replicated the ERP structure of the initial analyses and characterized the spatial distributions more closely by using a dense electrode array. Overall, our findings provide new insights in the componential structure of ERPs elicited in the P300 speller paradigm and have important implications for optimizing the speller's selection accuracy.

Pub.: 16 Nov '13, Pinned: 31 Jul '17