Division of Cyber-Human Interaction Technologies, University of Guadalajara (UdG), Guadalajara 44100, Jalisco, Mexico.
Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Monterrey 64849, Nuevo Leon, Mexico.
Sensors (Basel). 2022 Jan 26;22(3):937. doi: 10.3390/s22030937.
Tinnitus is an auditory condition that causes humans to hear a sound anytime, anywhere. Chronic and refractory tinnitus is caused by an over synchronization of neurons. Sound has been applied as an alternative treatment to resynchronize neuronal activity. To date, various acoustic therapies have been proposed to treat tinnitus. However, the effect is not yet well understood. Therefore, the objective of this study is to establish an objective methodology using electroencephalography (EEG) signals to measure changes in attentional processes in patients with tinnitus treated with auditory discrimination therapy (ADT). To this aim, first, event-related (de-) synchronization (ERD/ERS) responses were mapped to extract the levels of synchronization related to the auditory recognition event. Second, the deep representations of the scalograms were extracted using a previously trained Convolutional Neural Network (CNN) architecture (MobileNet v2). Third, the deep spectrum features corresponding to the study datasets were analyzed to investigate performance in terms of attention and memory changes. The results proved strong evidence of the feasibility of ADT to treat tinnitus, which is possibly due to attentional redirection.
耳鸣是一种听觉状况，会导致人类随时随地听到声音。慢性和难治性耳鸣是由于神经元过度同步引起的。声音已被应用于替代治疗以重新同步神经元活动。迄今为止，已经提出了各种声疗方法来治疗耳鸣。然而，其效果尚未得到很好的理解。因此，本研究的目的是建立一种使用脑电图 (EEG) 信号的客观方法，以测量接受听觉辨别治疗 (ADT) 的耳鸣患者注意力过程的变化。为此，首先，映射事件相关（去）同步（ERD/ERS）响应以提取与听觉识别事件相关的同步水平。其次，使用经过预先训练的卷积神经网络 (CNN) 架构（MobileNet v2）提取 scalograms 的深层表示。第三，分析与研究数据集相对应的深层频谱特征，以研究注意力和记忆变化方面的性能。结果证明了 ADT 治疗耳鸣的可行性的有力证据，这可能是由于注意力的重新定向。
