RESUMO
OBJECTIVE: A fully automated method for reducing EOG artifacts is presented and validated. METHODS: The correction method is based on regression analysis and was applied to 18 recordings with 22 channels and approx. 6 min each. Two independent experts scored the original and corrected EEG in a blinded evaluation. RESULTS: The expert scorers identified in 5.9% of the raw data some EOG artifacts; 4.7% were corrected. After applying the EOG correction, the expert scorers identified in another 1.9% of the data some EOG artifacts, which were not recognized in the uncorrected data. CONCLUSIONS: The advantage of a fully automated reduction of EOG artifacts justifies the small additional effort of the proposed method and is a viable option for reducing EOG artifacts. The method has been implemented for offline and online analysis and is available through BioSig, an open source software library for biomedical signal processing. SIGNIFICANCE: Visual identification and rejection of EOG-contaminated EEG segments can miss many EOG artifacts, and is therefore not sufficient for removing EOG artifacts. The proposed method was able to reduce EOG artifacts by 80%.
Assuntos
Artefatos , Eletroencefalografia , Eletroculografia , Processamento de Sinais Assistido por Computador , Adolescente , Adulto , Encéfalo/fisiologia , Mapeamento Encefálico , Feminino , Humanos , Masculino , Reprodutibilidade dos TestesRESUMO
Over the last 15 years, the Graz Brain-Computer Interface (BCI) has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. Recent projects deal with the development of asynchronous BCIs, the presentation of feedback and applications for communication and control.
Assuntos
Encéfalo/fisiopatologia , Eletroencefalografia/métodos , Doenças Neuromusculares/fisiopatologia , Doenças Neuromusculares/reabilitação , Projetos de Pesquisa , Terapia Assistida por Computador/métodos , Interface Usuário-Computador , Animais , Áustria , Potenciais Evocados , Humanos , UniversidadesRESUMO
In this paper, we describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI). The graphical capabilities of virtual reality (VR) should help to create new BCI-paradigms and improve feedback presentation. The objective of this combination is to enhance the subject's learning process of gaining control of the BCI. In this study, the participant had to imagine left or right hand movements while exploring a virtual conference room. By imaging a left hand movement the subject turned virtually to the left inside the room and with right hand imagery to the right. In fact, three trained subjects reached 80% to 100% BCI classification accuracy in the course of the experimental sessions. All subjects were able to achieve a rotation in the VR to the left or right by approximately 45 degrees during one trial.
Assuntos
Encéfalo/fisiologia , Diagnóstico por Computador/métodos , Eletroencefalografia/métodos , Potencial Evocado Motor/fisiologia , Imaginação/fisiologia , Reconhecimento Automatizado de Padrão/métodos , Interface Usuário-Computador , Adulto , Algoritmos , Gráficos por Computador , Simulação por Computador , Meio Ambiente , Estudos de Viabilidade , Humanos , Sistemas On-Line , Desempenho Psicomotor/fisiologiaRESUMO
The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity.
Assuntos
Esclerose Lateral Amiotrófica/reabilitação , Membros Artificiais , Auxiliares de Comunicação para Pessoas com Deficiência , Eletroencefalografia/métodos , Quadriplegia/reabilitação , Interface Usuário-Computador , Esclerose Lateral Amiotrófica/fisiopatologia , Encéfalo/fisiopatologia , Cognição , Eletroencefalografia/instrumentação , Potenciais Evocados , Mãos , Humanos , Internet , Educação de Pacientes como Assunto/métodos , Projetos Piloto , Telemedicina/métodosRESUMO
Event-related beta electroencephalographic (EEG) changes were studied during wrist movements induced by functional electrical stimulation (FES) of the appropriate forearm muscles in healthy volunteers. Active and passive hand movements were investigated as control conditions. Significant EEG changes with respect to a pre-movement period were analyzed by calculating time-frequency maps of event-related (de-)synchronization (ERD/ERS) for 32 EEG channels recorded from sensorimotor and premotor areas. Immediately after the beginning of the FES movement, a prominent ERD was found, followed by a beta ERS similar to that observed after active or passive wrist movements. Both changes were maximal over the contralateral primary hand area. The main difference between active and stimulation-induced movements was that in the latter case no ERD was detectable prior to movement-onset. These findings suggest that the sensorimotor processing during FES involves some of the processes which are also involved in voluntary hand movements.
