Application of P300 Event-Related Potential in Brain-Computer Interface

The primary purpose of this chapter is to demonstrate one of the applications of P300 event-related potential (ERP), i.e., brain-computer interface (BCI). Researchers and students will find the chapter appealing with a preliminary description of P300 ERP. This chapter also appreciates the importance and advantages of noninvasive ERP technique. In noninvasive BCI, the P300 ERPs are extracted from brain electrical activities [electroencephalogram (EEG)] as a signature of the underlying electrophysiological mechanism of brain responses to the external or internal changes and events. As the chapter proceeds, topics are covered on more relevant scholarly works about challenges and new directions in P300 BCI. Along with these, articles with the references on the advancement of this technique will be presented to ensure that the scholarly reviews are accessible to people who are new to this field. To enhance fundamental understanding, stimulation as well as signal processing methods will be discussed from some novel works with a comparison of the associated results. This chapter will meet the need for a concise and practical description of basic, as well as advanced P300 ERP techniques, which is suitable for a broad range of researchers extending from today’s novice to an experienced cognitive researcher

COGNITIVE PROCESSING AND BRAIN COMMUNICATION IN AMYOTROPHIC LATERAL SCLEROSIS

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive paralysis of limbs and bulbar musculature. This severe physical impairment makes cognitive evaluation a big challenge, thus there is a great need for an assessment that does not require overt motor responses. Moreover, we need of augmentative communication strategies because the disease generally leads to complete paralysis and, therefore, patients are unable to communicate with the external world by any means. For this purpose, Brain Computer Interfaces (BCIs) seem a promising approach to facilitate communication with these patients. The aim of this thesis is twofold. First, assessing cognitive processing in ALS by means of a novel evaluation tool. Second, allowing brain communication in completely paralyzed ALS patients who had lost their vision in order to eliminate the unbearable loss of communication in paralysis (“unlocking the locked-in”). The first study introduces a novel approach for assessing cognitive functions in ALS. This approach uses neuropsychological tests that require minimal overt motor or verbal responses; together with vibro-tactile P300s. Results indicate mild cognitive impairment in oral language comprehension tasks and reduced vibro-tactile P300 amplitudes in patients compared to healthy controls. Importantly, correlations between the vibro-tactile P300 latency and psychometric test results suggest that the former measure could serve as a neurophysiological marker of cognitive decline in ALS patients. The second study introduces a distraction paradigm based in auditory event-related potentials (ERPs) to evaluate the ability of change detection, focusing, and re-orientation of attention in ALS. The results revealed a modification of the amplitude and the latency of the N200, the P300 and the re-orienting negativity (RON) components. This could suggest an alteration of the endogenous mechanism that controls the detection of change, thus resulting in a reduction of the allocation and the re-orientation of attentional resources. The third study aimed at testing the feasibility of a Near Infrared Spectroscopy (NIRS) -based BCI communication approach for patients in the Completely Locked-in Stage (CLIS) due to ALS. For this purpose two CLIS patients were trained to control their cerebral-cortex´s functional-activations in response to auditory processing of correct or incorrect statements assessed with NIRS. The results of the study are very promising, showing that both CLIS patients communicated with fronto-cortical oxygenation based BCI at an average correct response rate of 70% over a period of several weeks. We conclude that this novel approach of brain-communication is safe and, reliable, representing, so far, the best communication possible for patients in completely locked-in state. In conclusion we propose a) the novel combination of vibro-tactile or acoustic ERPs and motor-independent neuropsychological tests as an alternative and easily implementable way for assessing cognitive functions in ALS and b) we confirm the usefulness and effectiveness of above mentioned electrophysiological approaches in the late stage of ALS either to assess cognitive processing or to establish communication with a BCI system

Practical Brain Computer Interfacing

A brain-computer interface (BCI) is a communication system that enables users to voluntary send messages or commands without movement. The classical goal of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of computer cursors, spelling programs or external devices, such as wheelchairs, robots and neural prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user's brain and translates them into suitable communication. This dissertation aims to develop faster and more reliable non-invasive BCI communication based on the study of users learning process and their interaction with the BCI transducer. To date, BCI research has focused on the development of advanced pattern recognition and classification algorithms to improve accuracy and reliability of the classified patterns. However, even with optimal detection methods, successful BCI operation depends on the degree to which the users can voluntary modulate their brain signals. Therefore, learning to operate a BCI requires repeated practice with feedback that engages learning mechanisms in the brain. In this work, several aspects including signal processing techniques, feedback methods, experimental and training protocols, demographics, and applications were explored and investigated. Research was focused on two BCI paradigms, steady-state visual evoked potentials (SSVEP) and event-related (de-)synchronization (ERD/ERS). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. A general application interface for BCI feedback tasks was developed to evaluate the practicability, reliability and acceptance of new feedback methods. The role of feedback and training was fully investigated on studies conducted with healthy subjects. The influence of demographics on BCIs was explored in two field studies with a large number of subjects. Results were supported through advanced statistical analysis. Furthermore, the BCI control was evaluated in a spelling application and a service robotic application. This dissertation demonstrates that BCIs can provide effective communication for most subjects. Presented results showed that improvements in the BCI transducer, training protocols, and feedback methods constituted the basis to achieve faster and more reliable BCI communication. Nevertheless, expert assistance is necessary for both initial configuration and daily operation, which reduces the practicability of BCIs for people who really need them

Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces

Riechmann H. Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces. Bielefeld: Universität Bielefeld; 2014

The use of P300-based BCIs in amyotrophic lateral sclerosis : from augmentative and alternative communication to cognitive assessment

The use of augmentative and alternative communication (AAC) tools in patients with amyotrophic lateral sclerosis (ALS), as effective means to compensate for the progressive loss of verbal and gestural communication, has been deeply investigated in the recent literature. The development of advanced AAC systems, such as eye-tracking (ET) and brain-computer interface (BCI) devices, allowed to bypass the important motor difficulties present in ALS patients. In particular, BCIs could be used in moderate to severe stages of the disease, since they do not require preserved ocular-motor ability, which is necessary for ET applications. Furthermore, some studies have proved the reliability of BCIs, regardless of the severity of the disease and the level of physical decline. However, the use of BCI in ALS patients still shows some limitations, related to both technical and neuropsychological issues. In particular, a range of cognitive deficits in most ALS patients have been observed. At the moment, no effective verbal-motor free measures are available for the evaluation of ALS patients\u2019 cognitive integrity; BCIs could offer a new possibility to administer cognitive tasks without the need of verbal or motor responses, as highlighted by preliminary studies in this field. In this review, we outline the essential features of BCIs systems, considering advantages and challenges of these tools with regard to ALS patients and the main applications developed in this field. We then outline the main findings with regard to cognitive deficits observed in ALS and some preliminary attempts to evaluate them by means of BCIs. The definition of specific cognitive profiles could help to draw flexible approaches tailored on patients\u2019 needs. It could improve BCIs efficacy and reduce patients\u2019 efforts. Finally, we handle the open question, represented by the use of BCIs with totally locked in patients, who seem unable to reliably learn to use such tool

Near-Infrared Spectroscopy for Brain Computer Interfacing

A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user and may even lead to abandonment of the device. A more suitable and usable interface is needed to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. This work implements a novel approach to BCI design, using noninvasive near-infrared spectroscopic (NIRS) techniques to develop a userfriendly optical BCI. NIRS is a practical non-invasive optical technique that can detect characteristic haemodynamic responses relating to neural activity. This thesis describes the use of NIRS to develop an accessible BCI system requiring very little user training. In harnessing the optical signal for BCI control an assessment of NIRS signal characteristics is carried out and detectable physiological effects are identified for BCI development. The investigations into various mental tasks for controlling the BCI show that motor imagery functions can be detected using NIRS. The optical BCI (OBCI) system operates in realtime characterising the occurrence of motor imagery functions, allowing users to control a switch - a “Mindswitch”. This work demonstrates the great potential of optical imaging methods for BCI development and brings to light an innovative approach to this field of research

Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces

Riechmann H. Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces. Bielefeld: Universität Bielefeld; 2014

Electroencephalography (EEG)-based Brain-Computer Interfaces

International audienceBrain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field