Electroencephalographic (EEG)recordings are affected data-segment, butitleads toaconsiderable infor- employed inordertoinvestigate thebrainactivity inneu- mation loss. A verypowerful approach, butstill notcommon ropathological subjects. Unfortunately EEG areoften contam-inclinical practice, wasproposed inliterature someyears inated bytheartifacts, signals thathavenon-cerebral origin iccticaroposinlitratu omeyears andthatmight mimiccognitive or pathologic activity and agic onsinartact signals extraction, detection therefore distort theanalysis ofEEG.Inthis paper weproposeandcancellation. Thisapproach concentrates theartifactual amultiresolution analysis, based onEEGwavelet processing, to content oftheEEGdataset inafewsignals toberejected, so extract thecerebral EEG rhythms. We alsopresent amethod that wedonothavetocancel theentire affected datasegment basedonRenyi's entropy andkurtosis toautomatically identify(1),(2),(3),(4). theWavelet components affected byartifacts. Finally, wediscuss asthejoint useofwavelet analysis, kurtosis andRenyi's entropy Obviously, artifact rejection always involves a lossof allows foradeeper investigation ofthebrainactivity andwe information, eventhough small, anditcauses alittle BEG discuss thecapability ofthis technique tobecomeanefficient distortion. Inaddition, manyartifacts havefixed characteris- preprocessing step tooptimize artifact rejection fromEEG.This tics inthefrequency domainandtheydistort theEEGonly isthefirst technique thatexploits thepeculiarities ofEEG to inaspecific frequency range. Optimize EEG artifact detection. i pcfcfeunyrne

Brain Activity Investigation byEEGProcessing: Wavelet Analysis, Kurtosis andRenyi's Entropy forArtifact Detection

Eye-blinks are known to substantially contaminate EEG signals, and thereby severely impact the decoding of EEG signals in various medical and scientific applications. In this work, we consider the problem of eye-blink detection that can then be employed to reliably remove eye-blinks from EEG signals. We propose a fully automated and unsupervised eyeblink detection algorithm, Blink that self-learns user-specific brainwave profiles for eye-blinks. Hence, Blink does away with any user training or manual inspection requirements. Blink functions on a single channel EEG, and is capable of estimating the start and end timestamps of eye-blinks in a precise manner. We collect four different eye-blink datasets and annotate 2300+ eye-blinks to evaluate the robustness performance of Blink across headsets (OpenBCI and Muse), eye-blink types (voluntary and involuntary), and various user activities (watching a video, reading an article, and attending to an external stimulation). The Blink algorithm performs consistently with an accuracy of over 98% for all the tasks with an average precision of 0.934. The source code and annotated datasets are released publicly for reproducibility and further research. To the best of our knowledge, this is the first ever annotated eye-blink EEG dataset released in the public domain.

Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals

Brain-Computer Interface (BCI) is an essential mechanism that interprets the human brain signal. It provides an assistive technology that enables persons with motor disabilities to communicate with the world and also empowers them to lead independent lives. The common BCI devices use Electroencephalography (EEG) electrical activity recorded from the scalp. EEG signals are noisy owing to the presence of many artifacts, namely, eye blink, head movement, and jaw movement. Such artifacts corrupt the EEG signal and make EEG analysis challenging. This issue is addressed by locating the artifacts and excluding the EEG segment from the analysis, which could lead to a loss of useful information. However, we propose a practical BCI that uses the artifacts which has a low signal to noise ratio. 
The objective of our work is to classify different types of artifacts, namely eye blink, head nod, head turn, and jaw movements in the EEG signal. The occurrence of the artifacts is first located in the EEG signal. The located artifacts are then classified using linear time and dynamic time warping techniques. The located artifacts can be used by a person with a motor disability to control a smartphone. A speech synthesis application that uses eyeblinks in a single channel EEG system and jaw clinches in four channels EEG system are developed. Word prediction models are used for word completion, thus reducing the number of artifacts required.

Analysis of artifacts in EEG signals for building BCIs.

Virtual keyboard with the prediction of words for children with cerebral palsy.

The most common brain-computer interface (BCI) devices use electroencephalography (EEG). EEG signals are noisy owing to the presence of many artifacts, namely head movement, and facial movements like eye blinks or jaw movements. Removal of these artifacts from EEG signals is essential for the success of any downstream BCI application. These artifacts influence different sensors of the EEG. In this paper, we devise algorithms for detection and classification of artifacts. Classification of artifacts into head nod, jaw movement and eye-blink is performed using two different varieties of time warping; namely, linear time warping, and dynamic time warping. The average accuracy of 85% and 90% is obtained using the former, and the later, respectively.

Time Warping Solutions for Classifying Artifacts in EEG

The objective of this work is to develop a personalized eye blink based communicator device. The eye blink is detected using a single channel Electroencephalogram (EEG) system with a Bluetooth interface. Eye blinks have predominant signatures in EEG. Different patterns based on these signatures are used to map alphanumeric characters on a virtual keyboard and words are generated. Voice module is incorporated into the app on the android device for better accessibility of the device by including Text To Speech synthesizer (TTS) at the back-end to produce speech output.

Srihari Maruthachalam

Papers

Brain-Computer Interface using Electroencephalogram Signatures of Eye Blinks.

Time Warping Solutions for Classifying Artifacts in EEG