SUMMARYPurpose: We propose a patient-specific algorithm for seizure prediction using multiple features of spectral power from electroencephalogram (EEG) and support vector machine (SVM) classification. Methods: The proposed patient-specific algorithm consists of preprocessing, feature extraction, SVM classification, and postprocessing. Preprocessing removes artifacts of intracranial EEG recordings and they are further preprocessed in bipolar and/or time-differential methods. Features of spectral power of raw, or bipolar and/or timedifferential intracranial EEG (iEEG) recordings in nine bands are extracted from a sliding 20-s-long and halfoverlapped window. Nine bands are selected based on standard EEG frequency bands, but the wide gamma bands are split into four. Cost-sensitive SVMs are used for classification of preictal and interictal samples, and double crossvalidation is used to achieve in-sample optimization and out-of-sample testing. We postprocess SVM classification outputs using the Kalman Filter and it removes sporadic and isolated false alarms. The algorithm has been tested on iEEG of 18 patients of 20 available in the Freiburg EEG database who had three or more seizure events. To investigate the discriminability of the features between preictal and interictal, we use the Kernel Fisher Discriminant analysis.Key findings: The proposed patient-specific algorithm for seizure prediction has achieved high sensitivity of 97.5% with total 80 seizure events and a low false alarm rate of 0.27 per hour and total false prediction times of 13.0% over a total of 433.2 interictal hours by bipolar preprocessing (92.5% sensitivity, a false positive rate of 0.20 per hour, and false prediction times of 9.5% by timedifferential preprocessing). This high prediction rate demonstrates that seizures can be predicted by the patient-specific approach using linear features of spectral power and nonlinear classifiers. Bipolar and/or timedifferential preprocessing significantly improves sensitivity and specificity. Spectral powers in high gamma bands are the most discriminating features between preictal and interictal. Significance: High sensitivity and specificity are achieved by nonlinear classification of linear features of spectral power. Power changes in certain frequency bands already demonstrated their possibilities for seizure prediction indicators, but we have demonstrated that combining those spectral power features and classifying them in a multivariate approach led to much higher prediction rates. Employing only linear features is advantageous, especially when it comes to an implantable device, because they can be computed rapidly with low power consumption.

In channel structures characterized by a powerful retailer (e.g., Wal-Mart, Home Depot), the dominant retailer's acceptance of a manufacturer's new product often determines the success of the new offering. Focusing on a manufacturer in such a market, we develop an approach to positioning and pricing a new product that directly incorporates the retailer's acceptance criteria into the development process. Our method also accounts for the retailer's product assortment and the competing manufacturers' potential reactions in wholesale prices. Our method merges individual-level conjoint models of preference with game-theoretic models of retailer and manufacturer behavior that are specific to the institutional setting of the focal manufacturer. The application of our approach in the context of a new power tool development project undertaken by this manufacturer also highlights the potential of our approach to other analogous institutional settings.new product forecasting, product positioning, distribution channel, conjoint model, game theory, big-box retailers, retailer acceptance, Wal-Mart, Home Depot

The authors explore how firms can enhance consumer performance in online idea generation platforms. Most, if not all, online idea generation platforms offer all consumers identical tasks in which (1) participants are granted access to ideas from other participants and (2) ideas are classified into categories, but consumers can navigate freely across idea categories. The former is linked to stimulus ideas, and the latter may be viewed as a first step toward problem decomposition. The authors propose that the effects of both stimulus ideas and problem decomposition are moderated by consumers' domain-specific knowledge. In particular, concrete cues such as stimulus ideas are more beneficial to low-knowledge consumers, and high-knowledge consumers are better served with abstract cues such as the ones offered by problem decomposition. The authors' hypotheses are supported by an extensive empirical investigation involving more than 6,000 participants. The findings suggest that online idea generation platforms should use problem decomposition more explicitly and that firms should not immediately show other participants' ideas to high-knowledge consumers when they access the platform. In other words, online idea generation platforms should customize the task structure on the basis of each participant's domain-specific knowledge.

Consumers often use both objective and subjective criteria to evaluate a product. For example, power tool users may evaluate a power tool on the basis of not only its objective attributes, such as price and switch type, but also its subjective characteristics, such as ease of use and feel of the tool. This research emphasizes incorporating subjective characteristics in new product design. The authors propose a model in which consumers' purchase intentions can be affected by both the objective attributes and the subjective characteristics. This model has the form of a hierarchical Bayesian structural equation model, in which the subjective characteristics are treated as latent constructs. The authors also propose a Bayesian forecasting procedure in which the estimated relationships are used to improve the out-of-sample prediction. They illustrate the proposed approach in two empirical studies. The results indicate that by collecting additional information about consumers' perceptions of the subjective characteristics, the proposed model provides the product designer with a better understanding and a more accurate prediction of consumers' product preferences than the traditional conjoint models.

In designing consumer durables such as appliances and power tools, it is important to account for variations in product performance across different usage situations and conditions. Since the specific usage of the product and the usage conditions can vary, the resultant variations in product performance also can impact consumer preferences for the product. Therefore, any new product that is designed should be robust to these variations-both in product performances and consumer preferences. This article refers to a robust product design as a design that has (1) the best possible (engineering and market) performance under the worst-case variations and (2) the least possible sensitivity in its performance under the variations. Achieving these robustness criteria, however, implies consideration of a large number of design factors across multiple functions. This article's objectives are (1) to provide a tutorial on how variations in product performance and consumer preferences can be incorporated in the generation and comparison of design alternatives and (2) to apply a multi-objective genetic algorithm (MOGA) that incorporates multifunction criteria in order to identify better designs while incorporating the robustness criteria in the selection process. Since the robustness criteria is based on variations in engineering performance as well as consumer preferences, the identified designs are robust and optimal from different functional perspectives, a significant advantage over extant approaches that do not consider robustness issues from multifunction perspectives. This study's approach is particularly useful for product managers and product development teams, who are charged with developing prototypes. They may find the approach helpful for obtaining customers' buy-in as well as internal buy-in early on in the product development cycle and thereby for reducing the cost and time involved in developing prototypes. This study's approach and its usefulness are illustrated using a case-study application of prototype development for a handheld power tool.