Abstract: An example system disclosed herein includes an analyzer to determine a first priming characteristic of media at a first location based on first neuro-response data and determine a second priming characteristic of the media at the second location based on second neuro-response data. The example system includes a selector to select a third location in the media after the first location or a fourth location in the media after the second location as a candidate location for introduction of advertising material based on the first priming characteristic and the second priming characteristic. The selector is to select the third location when the first priming characteristic indicates increased receptivity to the advertising material at the first location relative to the second priming characteristic and select the fourth location when the second priming characteristic indicates increased receptivity to the advertising material at the second location relative to the first priming characteristic.

Abstract: An apparatus includes a feature extractor to generate image descriptors based on retail product tag images corresponding to a retailer category; a probability density function generator to generate a probability density function of probability values corresponding to visual features represented in the image descriptors; a sample selector to select ones of the probability values based on a sample selection algorithm that identifies positions in the probability density function of the ones of the probability values to be selected; a category signature generator to generate a category signature based on the selected ones of the probability values; and a processor to train a convolutional neural network (CNN) based on a feature descriptor and one of the retail product tag images, the feature descriptor including the category signature concatenated to one of the image descriptors, the training to cause the CNN to classify the one of the retail product tag images as a type of product tag.

Abstract: Example headsets and methods for gathering electroencephalographic signals are disclosed herein. An example headset includes an adjustment assembly including a first support to be disposed on a first side of a head of a person and a second support to be disposed on a second side of the head of the person. The example headset also includes an adjustor operatively coupled to the first support. Further, the example headset includes an electrode strip and a tension strap having a first end operatively coupled to the adjustor and a second end operatively coupled to the second support.

Abstract: Example apparatus and methods for gathering electroencephalographic signals are disclosed herein. An example apparatus includes a band to be worn on a head of a person and a first strip adjustably coupled to the band. The example apparatus also includes a first set of electrodes coupled to the first strip to gather a first set of signals from the head and a magnetic fastener to couple the first strip to the band.

Abstract: An example system includes an analyzer to determine a first distance between (1) a first peak in a first frequency band of first neuro-response data gathered from a subject while exposed to media and (2) a second peak in the first frequency band; determine a second distance between (1) a third peak in the first frequency band and either (2) the second peak in the first frequency band or (3) a fourth peak in the first frequency band and determine a first difference between the first distance and the second distance. The example system includes a selector to determine a modification for the media based on the first difference and a modifier to implement the modification for presentation of the media.

Abstract: Methods, systems and apparatus are disclosed to model consumer choices. An example apparatus includes a multinomial logit (MNL) engine to add a set of products having respondent choice data to a base MNL model, an aggregate building engine to improve a computational efficiency of model generation by generating a number of copies of the base MNL model, each one of the number of copies of the base MNL model exhibiting an effect of an independence or irrelevant alternatives (HA) property, a sourcing modifier to proportionally affect interrelationships between dissimilar ones of the number of products in the set by inserting sourcing effect values in the aggregate model, an estimator to estimate the item utility parameters of the aggregate model based on the number of copies of the base MNL model and the respondent choice data, and a simulation engine to calculate the choice probability.

Abstract: An example apparatus includes a feature extractor to generate a first image descriptor based on a first image of a first retail product tag corresponding to a first category, the first image descriptor representative of one or more visual features of the first retail product tag; a feature descriptor generator to generate a feature descriptor corresponding to the first retail product tag by concatenating the first image descriptor and a first category signature corresponding to the first retailer category; and a classifier to generate a first probability value corresponding to a first type of promotional product tag and a second probability value corresponding to a second type of promotional product tag based on the feature descriptor; and determine whether the first retail product tag corresponds to the first type of promotional product tag or the second type of promotional product tag based on the first and second probability values.

Abstract: An example system to identify an advertisement to include in source material to increase an effectiveness of the advertisement includes an analyzer to determine one or more priming characteristics for a plurality of locations of a source material based on neuro-response data collected from a first subject exposed to the source material and a selector to identify an attribute of the advertisement, identify at least one of a temporal attribute or a spatial attribute for the plurality of locations, perform a comparison of the attribute of the advertisement to the at least one of the temporal attribute or the spatial attribute for the plurality of locations, select a first location of the plurality of locations for insertion of the advertisement based on the comparison and the priming characteristics, and transform the source material to include the advertisement at the first location.

Abstract: An electroencephalography (EEG) system includes a dry electrode design having a jagged, angular, comb, etc. shaped support housing. Each dry electrode housing includes multiple electrodes where each electrode has multiple contacts for scalp placement with minimal interference from hair. Signals from individual contacts may be disregarded and each housing may provide one or more aggregated signals for data analysis. Each electrode may be placed in close proximity with neighboring electrodes as no conductive gel is required and may be attached to the scalp using straps, elastic cap, spring-type materials, tape, etc. The dry electrode design effectively measures bio-signals including neurological activity.

Abstract: A system evaluates source materials such as videos, imagery, web pages, text, etc., in order to determine priming characteristics associated with the source materials. The system also obtains user preferences such as user interests, purchase history, location information, etc. The priming characteristics and user characteristics are blended to obtain blended attributes. The blended attributes are correlated with stimulus material attributes to intelligently and dynamically select stimulus material such as marketing, entertainment, informational materials, etc., for introduction into the source material. The stimulus material may be inserted in real-time or near real-time into the source material for delivery to a user.