Literature survey: Recording set up for electroencephalography (EEG) acquisition

Electroencephalogram (EEG) is used to study the activity of human brain using instrument named electroen-cephalograph. The application of EEG is now widened to many fields due to its great temporal resolution and other advantages. To utilize the advantage of EEG, the correct setup need to be done for recording the EEG. This is to ensure the EEG recorded is informative enough to serve the targeting application. In this paper, a literature study has been done to explore parameters of EEG recording used for different application. The parameters are number of channels, sampling rate and recording duration. It was found that for medical applications, the number of channel used ranging from two to 100 channels, sampling rate of 100Hz to 3000Hz, and recording duration from 3000 seconds to 21600 seconds. For BCI and neuromarketing, the number of channels used is from one up to 256 channels, 100Hz to 1000Hz sampling rate, and recording duration from five seconds onwards and some researches used continuous recording.

[1]  Varsha K. Harpale,et al.  Time and frequency domain analysis of EEG signals for seizure detection: A review , 2016, 2016 International Conference on Microelectronics, Computing and Communications (MicroCom).

[2]  Toshihisa Tanaka,et al.  EEG energy analysis based on MEMD with ICA pre-processing , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[3]  Seyyed Mohammad Reza Hashemi,et al.  Classification of EEG-based emotion for BCI applications , 2017, 2017 Artificial Intelligence and Robotics (IRANOPEN).

[4]  C. Binnie,et al.  Glossar der meistgebrauchten Begriffe in der klinischen Elektroenzephalographie und Vorschläge für die EEG-Befunderstellung , 2004 .

[5]  Gernot R. Müller-Putz,et al.  On the Use of Games for Noninvasive EEG-Based Functional Brain Mapping , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[6]  Tarmo Lipping,et al.  Prediction of outcome in traumatic brain injury patients using long-term qEEG features , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Arne Robben,et al.  Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[8]  Richard B Reilly,et al.  Electrograms (ECG, EEG, EMG, EOG). , 2010, Technology and health care : official journal of the European Society for Engineering and Medicine.

[9]  Luis Villaseñor Pineda,et al.  Sonification and textification: Proposing methods for classifying unspoken words from EEG signals , 2017, Biomed. Signal Process. Control..

[10]  Fabrice Labeau,et al.  Pre-Processing of multi-channel EEG for improved compression performance using SPIHT , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Hiie Hinrikus,et al.  Single channel EEG analysis for detection of depression , 2017, Biomed. Signal Process. Control..

[12]  M. L. Dewal,et al.  Wavelet entropy based EEG analysis for seizure detection , 2013, 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC).

[13]  Matthew R. Myers,et al.  Real-Time Detection and Monitoring of Acute Brain Injury Utilizing Evoked Electroencephalographic Potentials , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Caroline Palmer,et al.  Synchronizing MIDI and wireless EEG measurements during natural piano performance , 2017, Brain Research.

[15]  Paolo Maria Rossini,et al.  Searching for signs of aging and dementia in EEG through network analysis , 2017, Behavioural Brain Research.

[16]  Chun-Hsiang Chuang,et al.  Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment , 2017, IEEE Access.

[17]  Arun Chavan,et al.  EEG signals classification and diagnosis using wavelet transform and artificial neural network , 2017, 2017 International Conference on Nascent Technologies in Engineering (ICNTE).

[18]  Sapto W. Indratno,et al.  Bayesian approach to identify spike and sharp waves in EEG data of epilepsy patients , 2017, Biomed. Signal Process. Control..

[19]  David J. Reinkensmeyer,et al.  Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  Ana Carolina Lorena,et al.  Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease , 2017, Clinical Neurophysiology.

[21]  Gavas Rahul,et al.  Inactive-state recognition from EEG signals and its application in cognitive load computation , 2016 .

[22]  S. Vanhatalo,et al.  Automated classification of neonatal sleep states using EEG , 2017, Clinical Neurophysiology.

[23]  Tao Zhang,et al.  Automatic epileptic EEG detection using DT-CWT-based non-linear features , 2017, Biomed. Signal Process. Control..

[24]  J. McBride,et al.  Classification of traumatic brain injury using support vector machine analysis of event-related Tsallis entropy , 2011, Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine.

[25]  Amitash Ojha,et al.  Difference in brain activation patterns of individuals with high and low intelligence in linguistic and visuo-spatial tasks: An EEG study , 2017 .

[26]  K. K. Tan,et al.  The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy. , 1993, Electroencephalography and clinical neurophysiology.

[27]  Dattaprasad A. Torse,et al.  Design of adaptive EEG preprocessing algorithm for neurofeedback system , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[28]  I. Obeid,et al.  Assessing traumatic brain injuries using EEG power spectral analysis and instantaneous phase , 2012, 2012 38th Annual Northeast Bioengineering Conference (NEBEC).

[29]  O. Bai,et al.  Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Yuanqing Li,et al.  A P300-Based Threshold-Free Brain Switch and Its Application in Wheelchair Control , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Debi Prosad Dogra,et al.  Prediction of advertisement preference by fusing EEG response and sentiment analysis , 2017, Neural Networks.

[32]  Lianyang Li,et al.  Brain activation profiles in mTBI: Evidence from combined resting-state EEG and MEG activity , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).