The Cervical-Vestibular evoked Myogenic potential(c-VEMP) is a biphasic surface potential recorded from the belly of Sternocleidomastoid muscle (SCM) followed by presenting a short loud sound. Various studies have been done with different stimulus to obtain better VEMP responses. The present study is aimed at comparing the c-VEMP responses (amplitude and latencies) of 500 Hz tone burst with 500Hz octave chirp (360-720Hz). c-VEMP was administered on 60 ears from 30 subjects. After preparation, responses were recorded presenting 500Hz Tone bursts and 500Hz octave chirps. P1-N1 amplitude, P1 and N1 latencies for both stimuli were noted. The chirp was observed to produce significantly larger amplitude and early latencies than tone burst (p<0.01). This study was in search of a stimulus that will produce larger and better response to be used in clinics , Chirp qualifies to be one. Further studies on larger sample size and age groups are required to make generalizations.

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Much of the ongoing explosion of digital content is in the form of text. This content is a virtual gold-mine of information that can inform a range of social, governmental, and business decisions. For example, using content available on blogs and social networking sites businesses can find out what its customers are saying about their products and services. In the digital age where customer is king, the business value of ascertaining consumer sentiment cannot be overstated. People express sentiments in myriad ways. At times, they use simple, direct assertions, but most often they use sentences involving comparisons, conjunctions expressing multiple and possibly opposing sentiments about multiple features and entities,and pronominal references whose resolution requires discourse level context. Frequently people use abbreviations, slang, SMSese, idioms and metaphors. Understanding the latter also requires common sense reasoning. In this paper, we present iSEE, a fully implemented sentiment extraction engine, which makes use of statistical methods, classical NLU techniques, common sense reasoning, and probabilistic inference to extract entity and feature specific sentiment from complex sentences and dialog. Most of the components of iSEE are domain independent and the system can be generalized to new domains by simply adding domain relevant lexicons.