Development Of Brain-computer Interface Technology Research Articles

廓清腦機介面的倫理視野：變與不變

LANGUAGE NOTE | Document text in Chinese; abstract also in English.
 “The Ethics of Thinking with Machines” (Lyreskog et al. 2023, 11-35) focuses on the ethical issues and debates caused by the development of brain-computer interface technology, and describes a changing moral landscape: some boundaries are blurred or broken, while some core values are reinvented. However, a constant concern underlies these changes, namely concern about “who I am.” This concern has become even more pressing in the current age of artificial intelligence.

Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification.

With the rapid development of deep learning, researchers have gradually applied it to motor imagery brain computer interface (MI-BCI) and initially demonstrated its advantages over traditional machine learning. However, its application still faces many challenges, and the recognition rate of electroencephalogram (EEG) is still the bottleneck restricting the development of MI-BCI. In order to improve the accuracy of EEG classification, a DSC-ConvLSTM model based on the attention mechanism is proposed for the multi-classification of motor imagery EEG signals. To address the problem of the small sample size of well-labeled and accurate EEG data, the preprocessing uses sliding windows for data augmentation, and the average prediction loss of each sliding window is used as the final prediction loss for that trial. This not only increases the training sample size and is beneficial to train complex neural network models, but also the network no longer extracts the global features of the whole trial so as to avoid learning the difference features among trials, which can effectively eliminate the influence of individual specificity. In the aspect of feature extraction and classification, the overall network structure is designed according to the characteristics of the EEG signals in this paper. Firstly, depth separable convolution (DSC) is used to extract spatial features of EEG signals. On the one hand, this reduces the number of parameters and improves the response speed of the system. On the other hand, the network structure we designed is more conducive to extract directly the direct extraction of spatial features of EEG signals. Secondly, the internal structure of the Long Short-Term Memory (LSTM) unit is improved by using convolution and attention mechanism, and a novel bidirectional convolution LSTM (ConvLSTM) structure is proposed by comparing the effects of embedding convolution and attention mechanism in the input and different gates, respectively. In the ConvLSTM module, the convolutional structure is only introduced into the input-to-state transition, while the gates still remain the original fully connected mechanism, and the attention mechanism is introduced into the input to further improve the overall decoding performance of the model. This bidirectional ConvLSTM extracts the time-domain features of EEG signals and integrates the feature extraction capability of the CNN and the sequence processing capability of LSTM. The experimental results show that the average classification accuracy of the model reaches 73.7% and 92.6% on two datasets, BCI Competition IV Dataset 2a and High Gamma Dataset, respectively, which proves the robustness and effectiveness of the model we proposed. It can be seen that the model in this paper can deeply excavate significant EEG features from the original EEG signals, show good performance in different subjects and different datasets, and improve the influence of individual variability on the classification performance, which is of practical significance for promoting the development of brain-computer interface technology towards a practical and marketable direction.

Applications, industrial transformation and commercial value of brain-computer interface technology

Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.

A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation

Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.

Review on EEG-Based Authentication Technology.

With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG-based authentication and systematically introduced EEG-based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG-based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.

The Inviolate Person

Would government access to the mental impressions and memories of individuals be an unreasonable or seizure? In this article I explore technology-facilitated law enforcement access to information stored within individual human minds and what barriers the Fourth Amendment may present to such invasions. I begin by reviewing the development of brain-computer interface technology and its current and probable near-future capabilities. I then explore the right protected by the Fourth Amendment, and how the expectation of privacy analysis currently favored by the Supreme Court may interact with such technology. I conclude that some particularly invasive thought access techniques are probably not a constitutional search under that approach. I propose that certain aspects of the property-oriented view of [t]he right of the people to be secure in their persons extant prior to the Supreme Court's decision in Warden v. Hayden may remain applicable when the corporeal mind is the subject of a and seizure. I also promote the notion that the nature of the right should be grounded in the words of the Amendment, with a focus on the enumerated right to personal security rather than the unenumerated and often arbitrary reasonable person privacy analysis. I then attempt to formulate a definition of the inviolate person, a boundary beyond which a governmental intrusion is per se unreasonable. I offer several justifications for this demarcation beyond the constitutional argument, and conclude with a brief argument for a two-tiered exclusionary rule with additional consequences for egregious constitutional violations.

The Discussion of the Effects of Linear and Nonlinear Algorithms on Emotional Evaluation

With the continuous development of brain-computer interface technology, as a typical brainwave signal carrying brain state information, has entered the researcher’s point of view. Brain waves carry detailed information about the state of the brain. [1]Therefore, the use of computers to collect and analyze EEG signals play an important role in the study of artificial intelligence. In this paper, the EEG signal of positive and negative emotions is taken as the research object.[2] Firstly, the present situation of brain wave research is introduced, and the image is experimented with the Chinese emotional image system (CAPS) of Chinese Academy of Sciences. Based on the previous work of “Emotional Evaluation Based on SVM”, further data analysis was carried out using support vector machines (SVM) and linear machines with linear and nonlinear cores, and 49.2% to 62.4% accuracy were obtained respectively. This paper provides a feasible solution for the study of EEG in the field of emotional analysis.Finally, through the accuracy of the linear algorithm and the nonlinear algorithm on the data, we find that the EEG data are non-linear separable.