EEG-based speech recognition:Impact of temporal effects

In this paper, we investigate the use of electroencephalograhic signals for the purpose of recognizing unspoken speech. The term unspoken speech refers to the process in which a subject imagines speaking a given word without moving any articulatory muscle or producing any audible sound. Early work by Wester (Wester, 2006) presented results which were initially interpreted to be related to brain activity patterns due to the imagination of pronouncing words. However, subsequent investigations lead to the hypothesis that the good recognition performance might instead have resulted from temporal correlated artifacts in the brainwaves since the words were presented in blocks. In order to further investigate this hypothesis, we run a study with 21 subjects, recording 16 EEG channels using a 128 cap montage. The vocabulary consists of 5 words, each of which is repeated 20 times during a recording session in order to train our HMM-based classifier. The words are presented in blockwise, sequential, and random order. We show that the block mode yields an average recognition rate of 45.50%, but it drops to chance level for all other modes. Our experiments suggest that temporal correlated artifacts were recognized instead of words in block recordings and back the above-mentioned hypothesis

Further Investigations on Unspoken Speech- Findings in an attempt of developing EEG-based word recognition

Hiermit versichere ich, die Arbeit selbständig erstellt und keine anderen, als die angegebe-nen Hilfsmittel benutzt zu haben. Vorname, Name Pittsburgh, den Datum Classification of signals originating from brain activity has become an active area of research in the past years. While the trend has evolved in the direction of using modern imaging techniques such as fMRI, we consider brain-activity based speech recognition more suitable to be attempted using techniques with high temporal resolution such as electroencephalography. Past achievements demonstrate that EEG-based approaches are promising [39]. This thesis is a continuation of a previous thesis ([43]), written and implemented by Marek Wester that had been modifying a speech recognizer to be applied on brain waves in order to attempt the recognition of words from a small vocabulary domain, uttered in different modalities. The modalities included normal speaking, mumbling, yet the primary focus had been on the recognition of words uttered in a modality called unspoken speech. In the latter modality the test subject was asked to think of the word in question without making any audible sound o