Authentication is a crucial consideration when securing data or any kind of information system. Though existing approaches for authentication are user-friendly, they have vulnerabilities such as the possibility & criminally threatening a user. We propose a novel approach which uses Electroencephalogram (EEG) brain signals for an authentication process. Unique features of EEG data for distinguishing brain activities can potentially be used to authenticate a user. Compared to other biometric systems, this approach is very robust and more secure because response is significantly changed according to instantaneous mental condition. In the proposed approach, the system user is asked to visualize a number while corresponding EEG signals are captured. Captured signals are used to train the system, compared in the authentication process. This approach mainly focuses on the 8 to 30 Hz Alpha and Beta combined frequency band across all EEG channels, since it is the most appropriate band for EEG signals in Brain Computer Interfaces (BCI). Common Spatial Patterns (CSP) values were used as main features to train the model. Linear discriminant analysis (LDA) was used as a classification algorithm for a given set of user data. A trained set of models for each user is embedded in the system as a parameter database. Each user selects the profile attached to their trained model before the authentication session. Eventually, a trained model can authenticate a user after memorization of pre-determined four digits which the user is asked to think at the first stage of the process. The maximum accuracy recorded with the existing data was 96.97%.

Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using such technologies in real-world applications. In this research, the challenge has been addressed by reducing the complexity of the brain signal acquisition and analysis processes. This was achieved by reducing the number of electrodes, simplifying the critical task without compromising accuracy. Event-related potentials (ERP), a.k.a. time-locked stimulation, was used to collect data from each subject’s head. Following a relaxation period, each subject was visually presented a random four-digit number and then asked to think of it for 10 seconds. Fifteen trials were conducted with each subject with relaxation and visual stimulation phases preceding each mental recall segment. We introduce a novel derived feature, dubbed Inter-Hemispheric Amplitude Ratio (IHAR), which expresses the ratio of amplitudes of laterally corresponding electrode pairs. The feature was extracted after expanding the training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to our results, perfect accuracy can also be achieved using fewer electrodes. However, AF3, AF4, F7, and F8 electrode combination with kNN classifier which yielded 99.0±0.8% testing accuracy is the best for person identification to maintain both user-friendliness and performance. Surprisingly, the relaxation phase manifested the highest accuracy of the three phases.

With audio's increasing importance in computer applications, users will soon need presentation, management and organizational capabilities similar to visual window systems to avoid a confusing cacophony of multiple audio sources sounding at once. The ways in which an audio window system could be used are described. These include multimedia documents, spatial data management systems, and teleconferencing. The signal processing methods used to create hierarchical and spatial distribution among nearly arbitrary (not pure sine wave) audio sources are discussed. A prototype system, combining hierarchical and spatial processing functions with a computer-controlled switch, software and human input devices, is presented. Two envisioned implementations, a terminal-based system and a network-based server, are described. Preliminary work suggests that an effective audio window system needs much less complexity and fewer levels of digital signal processing precision than the current prototype.

User authentication systems based on EEG (electroencephalography) is currently popular, marking an inflection point in the field. Recently, the scientific community has been making tremendous attempts towards perceiving uniqueness of brain signal patterns. Several types of methodical approaches have been proposed and prototyped to analyze EEG data with various signal-processing methods and pattern-recognition algorithms. Even though there are many stimulation methods to produce reasonable distinctiveness between subjects, optimization and lowering task complexity are still desirable from techno-economic points of view. With recent technological advancement of EEG signal capturing devices, the process is getting comparatively simpler as devices are capable of providing better portability with reduced calibration time. However, most detailed analysis suggests that a minimal number of most appropriate channels should be selected for better results, even if a system is equipped with the most advanced hardware. Researchers are now focusing on implementing computationally low cost systems with better accuracy, regardless of complexity of the tasks. This paper is a review of several approaches, providing an overview of crucial design considerations in handling EEG data for extended accuracy and practical applicability to authentication.